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[GH-ISSUE #2129] High CPU and GPU usage, even when noone is interacting with ollama #63255

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opened 2026-05-03 12:45:25 -05:00 by GiteaMirror · 9 comments
GiteaMirror commented 2026-05-03 12:45:25 -05:00
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Originally created by @ThatCoffeeGuy on GitHub (Jan 21, 2024).
Original GitHub issue: https://github.com/ollama/ollama/issues/2129

Hey.

I have used ollama a few hours ago... only to notice now, that the CPU usage is quite high and the GPU usage is around 30% while the model and web are doing absolutely nothing.

lsof is showing 1.8k open files and the processes keep renewing their PIDs, it's impossible to strace them. What's going on?

image

Originally created by @ThatCoffeeGuy on GitHub (Jan 21, 2024). Original GitHub issue: https://github.com/ollama/ollama/issues/2129 Hey. I have used ollama a few hours ago... only to notice now, that the CPU usage is quite high and the GPU usage is around 30% while the model and web are doing absolutely nothing. lsof is showing 1.8k open files and the processes keep renewing their PIDs, it's impossible to strace them. What's going on? ![image](https://github.com/jmorganca/ollama/assets/24213618/c601bdc8-4fe5-4537-8c29-d991950e173a)
GiteaMirror added the bug label 2026-05-03 12:45:25 -05:00
GiteaMirror commented 2026-05-03 12:45:27 -05:00
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@ThatCoffeeGuy commented on GitHub (Jan 22, 2024):

Distributor ID: Ubuntu
Description:    Ubuntu 22.04.3 LTS
Release:        22.04
Codename:       jammy

sadmin@aiml:~$ 
uname -r
5.15.0-89-generic

ollama serve and ollama webui. But the process spinning the CPU and GPU are ollama it seems. I don't know if it's triggered by the webui.
which is...
ghcr.io/ollama-webui/ollama-webui:main

<!-- gh-comment-id:1904504925 --> @ThatCoffeeGuy commented on GitHub (Jan 22, 2024): ``` Distributor ID: Ubuntu Description: Ubuntu 22.04.3 LTS Release: 22.04 Codename: jammy sadmin@aiml:~$ uname -r 5.15.0-89-generic ``` ollama serve and ollama webui. But the process spinning the CPU and GPU are ollama it seems. I don't know if it's triggered by the webui. which is... `ghcr.io/ollama-webui/ollama-webui:main`
GiteaMirror commented 2026-05-03 12:45:28 -05:00
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@dhiltgen commented on GitHub (Jan 26, 2024):

Can you share the logs from the server?

If the logs don't contain anything, consider killing the process which should generate a stack dump which may help us understand what it's doing.

<!-- gh-comment-id:1912639078 --> @dhiltgen commented on GitHub (Jan 26, 2024): Can you share the logs from the server? If the logs don't contain anything, consider killing the process which should generate a stack dump which may help us understand what it's doing.
GiteaMirror commented 2026-05-03 12:45:29 -05:00
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@pdevine commented on GitHub (Mar 11, 2024):

We've had a number of different performance fixes to the ollama server since January. I'm going to go ahead and close this, but please feel free to reopen it if this is still a problem.

<!-- gh-comment-id:1989049339 --> @pdevine commented on GitHub (Mar 11, 2024): We've had a number of different performance fixes to the ollama server since January. I'm going to go ahead and close this, but please feel free to reopen it if this is still a problem.
GiteaMirror commented 2026-05-03 12:45:29 -05:00
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@coc0nut commented on GitHub (Apr 3, 2024):

Im having a similar issu right now...

<!-- gh-comment-id:2033454290 --> @coc0nut commented on GitHub (Apr 3, 2024): Im having a similar issu right now...
GiteaMirror commented 2026-05-03 12:45:29 -05:00
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@koldogut commented on GitHub (Jun 21, 2024):

same here, any solution?

<!-- gh-comment-id:2182634892 --> @koldogut commented on GitHub (Jun 21, 2024): same here, any solution?
GiteaMirror commented 2026-05-03 12:45:30 -05:00
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@shahrokh4code commented on GitHub (Apr 9, 2025):

same issue after update

<!-- gh-comment-id:2789501615 --> @shahrokh4code commented on GitHub (Apr 9, 2025): same issue after update
GiteaMirror commented 2026-05-03 12:45:30 -05:00
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@DRRDietrich commented on GitHub (May 5, 2025):

I also have the problem that ollama constantly causes 100% CPU usage, but the cause seems to be Open WebUI. Without Open WebUI, it works as expected. As soon as Open WebUI comes into play, I have a constant 100% CPU usage.

<!-- gh-comment-id:2852089751 --> @DRRDietrich commented on GitHub (May 5, 2025): I also have the problem that ollama constantly causes 100% CPU usage, but the [cause](https://github.com/open-webui/open-webui/discussions/1776) seems to be Open WebUI. Without Open WebUI, it works as expected. As soon as Open WebUI comes into play, I have a constant 100% CPU usage.
GiteaMirror commented 2026-05-03 12:45:31 -05:00
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@dhiltgen commented on GitHub (May 8, 2025):

Most likely if you look at the server logs you'll see a steady stream of requests coming in, so the CPU (or GPU) is busy doing inference for a client. If you do not see steady requests coming in, and the CPU usage remains high, please share server logs and ps/top output demonstrating the behavior so we can investigate. Setting OLLAMA_DEBUG=1 for the server may help shed additional light if it does turn out to be a bug in ollama itself.

<!-- gh-comment-id:2863436070 --> @dhiltgen commented on GitHub (May 8, 2025): Most likely if you look at the server logs you'll see a steady stream of requests coming in, so the CPU (or GPU) is busy doing inference for a client. If you do not see steady requests coming in, and the CPU usage remains high, please share server logs and ps/top output demonstrating the behavior so we can investigate. Setting `OLLAMA_DEBUG=1` for the server may help shed additional light if it does turn out to be a bug in ollama itself.
GiteaMirror commented 2026-05-03 12:45:32 -05:00
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@DRRDietrich commented on GitHub (May 8, 2025):

My setup:

OS: Fedora Linux 42 (Workstation Edition) x86_64
Kernel: 6.14.5-300.fc42.x86_64
CPU: AMD Ryzen 5 2600X (12) @ 3.800GHz
Memory: 30182MiB / 64212MiB

The setup is still in testing, so I'm currently the only user.

ollama (0.6.7) installed on fedora

curl -fsSL https://ollama.com/install.sh | sh

systemctl edit ollama.service

[Service]
Environment="OLLAMA_NUM_THREADS=12"
Environment="OLLAMA_HOST=0.0.0.0"
Environment="OLLAMA_DEBUG=INFO"

I'm using qwen3:30b-a3b (with additional PARAMETER num_thread 12)

# Modelfile generated by "ollama show"
# To build a new Modelfile based on this, replace FROM with:
# FROM qwen3:30b-a3b

FROM /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be6176b68c20f156d54a07ac
TEMPLATE """{{- if .Messages }}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{ if .Content }}{{ .Content }}
{{- else if .ToolCalls }}<tool_call>
{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
{{ end }}</tool_call>
{{- end }}{{ if not $last }}<|im_end|>
{{ end }}
{{- else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
{{ end }}
{{- end }}
{{- else }}
{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
PARAMETER repeat_penalty 1
PARAMETER stop <|im_start|>
PARAMETER stop <|im_end|>
PARAMETER temperature 0.6
PARAMETER top_k 20
PARAMETER top_p 0.95
PARAMETER num_thread 12
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I have no problems as long as I use the model with ollama run. As soon as the open-webui is used, the problem described arises.

open-webui (v0.6.7) as docker container

version: '3'
services:
  openwebui:
    image: ghcr.io/open-webui/open-webui:main
    ports:
      - "3000:8080"
    volumes:
      - open-webui:/app/backend/data
    extra_hosts:
      - "host.docker.internal:host-gateway"
volumes:
  open-webui:

Very rarely, but sometimes there are individual answers according to which the CPU load goes down completely. But in the vast majority of cases, an answer results in the CPU load remaining exactly as high after the reply as it was during the reply generation.

Here is an example. Question: What is 1+1?.

Mai 08 18:22:21 home systemd[1]: Started ollama.service - Ollama Service.
Mai 08 18:22:21 home ollama[160182]: 2025/05/08 18:22:21 routes.go:1233: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PR>
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.500+02:00 level=INFO source=images.go:458 msg="total blobs: 5"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.500+02:00 level=INFO source=images.go:465 msg="total unused blobs removed: 0"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.501+02:00 level=INFO source=routes.go:1300 msg="Listening on [::]:11434 (version 0.6.7)"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.501+02:00 level=DEBUG source=sched.go:107 msg="starting llm scheduler"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.501+02:00 level=INFO source=gpu.go:217 msg="looking for compatible GPUs"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.502+02:00 level=DEBUG source=gpu.go:98 msg="searching for GPU discovery libraries for NVIDIA"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.502+02:00 level=DEBUG source=gpu.go:501 msg="Searching for GPU library" name=libcuda.so*
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.502+02:00 level=DEBUG source=gpu.go:525 msg="gpu library search" globs="[/usr/local/lib/ollama/libcuda.so* /libcuda.so* /usr/local/cuda*/targets/*/l>
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.509+02:00 level=DEBUG source=gpu.go:558 msg="discovered GPU libraries" paths=[]
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.509+02:00 level=DEBUG source=gpu.go:501 msg="Searching for GPU library" name=libcudart.so*
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.509+02:00 level=DEBUG source=gpu.go:525 msg="gpu library search" globs="[/usr/local/lib/ollama/libcudart.so* /libcudart.so* /usr/local/lib/ollama/cu>
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.514+02:00 level=DEBUG source=gpu.go:558 msg="discovered GPU libraries" paths="[/usr/local/lib/ollama/cuda_v11/libcudart.so.11.3.109 /usr/local/lib/o>
Mai 08 18:22:21 home ollama[160182]: cudaSetDevice err: 35
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=gpu.go:574 msg="Unable to load cudart library /usr/local/lib/ollama/cuda_v11/libcudart.so.11.3.109: your nvidia driver >
Mai 08 18:22:21 home ollama[160182]: cudaSetDevice err: 35
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=gpu.go:574 msg="Unable to load cudart library /usr/local/lib/ollama/cuda_v12/libcudart.so.12.8.90: your nvidia driver i>
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=WARN source=amd_linux.go:61 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version f>
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:101 msg="evaluating amdgpu node /sys/class/kfd/kfd/topology/nodes/0/properties"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:121 msg="detected CPU /sys/class/kfd/kfd/topology/nodes/0/properties"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:101 msg="evaluating amdgpu node /sys/class/kfd/kfd/topology/nodes/1/properties"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:206 msg="mapping amdgpu to drm sysfs nodes" amdgpu=/sys/class/kfd/kfd/topology/nodes/1/properties vendor=4>
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:240 msg=matched amdgpu=/sys/class/kfd/kfd/topology/nodes/1/properties drm=/sys/class/drm/card1/device
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=WARN source=amd_linux.go:309 msg="amdgpu too old gfx803" gpu=0
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=INFO source=amd_linux.go:402 msg="no compatible amdgpu devices detected"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=INFO source=gpu.go:377 msg="no compatible GPUs were discovered"
Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=INFO source=types.go:130 msg="inference compute" id=0 library=cpu variant="" compute="" driver=0.0 name="" total="62.7 GiB" availabl>
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.445+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.446+02:00 level=DEBUG source=gpu.go:391 msg="updating system memory data" before.total="62.7 GiB" before.free="45.5 GiB" before.free_swap="8.0 GiB" >
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.447+02:00 level=DEBUG source=sched.go:183 msg="updating default concurrency" OLLAMA_MAX_LOADED_MODELS=3 gpu_count=1
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.467+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.487+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=DEBUG source=sched.go:213 msg="cpu mode with first model, loading"
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=DEBUG source=gpu.go:391 msg="updating system memory data" before.total="62.7 GiB" before.free="45.0 GiB" before.free_swap="8.0 GiB" >
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=INFO source=server.go:105 msg="system memory" total="62.7 GiB" free="45.0 GiB" free_swap="8.0 GiB"
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=DEBUG source=memory.go:108 msg=evaluating library=cpu gpu_count=1 available="[45.0 GiB]"
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=WARN source=ggml.go:152 msg="key not found" key=qwen3moe.vision.block_count default=0
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.489+02:00 level=INFO source=server.go:138 msg=offload library=cpu layers.requested=-1 layers.model=49 layers.offload=0 layers.split="" memory.availa>
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.489+02:00 level=DEBUG source=server.go:262 msg="compatible gpu libraries" compatible=[]
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: loaded meta data with 31 key-value pairs and 579 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be617>
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   0:                       general.architecture str              = qwen3moe
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   1:                               general.type str              = model
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   2:                               general.name str              = Qwen3 30B A3B
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   3:                           general.basename str              = Qwen3
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   4:                         general.size_label str              = 30B-A3B
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   5:                            general.license str              = apache-2.0
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   6:                       qwen3moe.block_count u32              = 48
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   7:                    qwen3moe.context_length u32              = 40960
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   8:                  qwen3moe.embedding_length u32              = 2048
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   9:               qwen3moe.feed_forward_length u32              = 6144
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  10:              qwen3moe.attention.head_count u32              = 32
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  11:           qwen3moe.attention.head_count_kv u32              = 4
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  12:                    qwen3moe.rope.freq_base f32              = 1000000.000000
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  13:  qwen3moe.attention.layer_norm_rms_epsilon f32              = 0.000001
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  14:                 qwen3moe.expert_used_count u32              = 8
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  15:              qwen3moe.attention.key_length u32              = 128
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  16:            qwen3moe.attention.value_length u32              = 128
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  17:                      qwen3moe.expert_count u32              = 128
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  18:        qwen3moe.expert_feed_forward_length u32              = 768
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  19:                       tokenizer.ggml.model str              = gpt2
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  20:                         tokenizer.ggml.pre str              = qwen2
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  21:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  22:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  23:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  24:                tokenizer.ggml.eos_token_id u32              = 151645
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  25:            tokenizer.ggml.padding_token_id u32              = 151643
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  26:                tokenizer.ggml.bos_token_id u32              = 151643
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  27:               tokenizer.ggml.add_bos_token bool             = false
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  28:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  29:               general.quantization_version u32              = 2
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  30:                          general.file_type u32              = 15
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type  f32:  241 tensors
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type  f16:   48 tensors
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q4_K:  265 tensors
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q6_K:   25 tensors
Mai 08 18:22:56 home ollama[160182]: print_info: file format = GGUF V3 (latest)
Mai 08 18:22:56 home ollama[160182]: print_info: file type   = Q4_K - Medium
Mai 08 18:22:56 home ollama[160182]: print_info: file size   = 17.34 GiB (4.88 BPW)
Mai 08 18:22:56 home ollama[160182]: init_tokenizer: initializing tokenizer for type 2
Mai 08 18:22:56 home ollama[160182]: load: control token: 151660 '<|fim_middle|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151659 '<|fim_prefix|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151653 '<|vision_end|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151648 '<|box_start|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151646 '<|object_ref_start|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151649 '<|box_end|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151655 '<|image_pad|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151651 '<|quad_end|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151647 '<|object_ref_end|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151652 '<|vision_start|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151654 '<|vision_pad|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151656 '<|video_pad|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151644 '<|im_start|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151661 '<|fim_suffix|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: control token: 151650 '<|quad_start|>' is not marked as EOG
Mai 08 18:22:56 home ollama[160182]: load: special tokens cache size = 26
Mai 08 18:22:56 home ollama[160182]: load: token to piece cache size = 0.9311 MB
Mai 08 18:22:56 home ollama[160182]: print_info: arch             = qwen3moe
Mai 08 18:22:56 home ollama[160182]: print_info: vocab_only       = 1
Mai 08 18:22:56 home ollama[160182]: print_info: model type       = ?B
Mai 08 18:22:56 home ollama[160182]: print_info: model params     = 30.53 B
Mai 08 18:22:56 home ollama[160182]: print_info: general.name     = Qwen3 30B A3B
Mai 08 18:22:56 home ollama[160182]: print_info: n_ff_exp         = 0
Mai 08 18:22:56 home ollama[160182]: print_info: vocab type       = BPE
Mai 08 18:22:56 home ollama[160182]: print_info: n_vocab          = 151936
Mai 08 18:22:56 home ollama[160182]: print_info: n_merges         = 151387
Mai 08 18:22:56 home ollama[160182]: print_info: BOS token        = 151643 '<|endoftext|>'
Mai 08 18:22:56 home ollama[160182]: print_info: EOS token        = 151645 '<|im_end|>'
Mai 08 18:22:56 home ollama[160182]: print_info: EOT token        = 151645 '<|im_end|>'
Mai 08 18:22:56 home ollama[160182]: print_info: PAD token        = 151643 '<|endoftext|>'
Mai 08 18:22:56 home ollama[160182]: print_info: LF token         = 198 'Ċ'
Mai 08 18:22:56 home ollama[160182]: print_info: FIM PRE token    = 151659 '<|fim_prefix|>'
Mai 08 18:22:56 home ollama[160182]: print_info: FIM SUF token    = 151661 '<|fim_suffix|>'
Mai 08 18:22:56 home ollama[160182]: print_info: FIM MID token    = 151660 '<|fim_middle|>'
Mai 08 18:22:56 home ollama[160182]: print_info: FIM PAD token    = 151662 '<|fim_pad|>'
Mai 08 18:22:56 home ollama[160182]: print_info: FIM REP token    = 151663 '<|repo_name|>'
Mai 08 18:22:56 home ollama[160182]: print_info: FIM SEP token    = 151664 '<|file_sep|>'
Mai 08 18:22:56 home ollama[160182]: print_info: EOG token        = 151643 '<|endoftext|>'
Mai 08 18:22:56 home ollama[160182]: print_info: EOG token        = 151645 '<|im_end|>'
Mai 08 18:22:56 home ollama[160182]: print_info: EOG token        = 151662 '<|fim_pad|>'
Mai 08 18:22:56 home ollama[160182]: print_info: EOG token        = 151663 '<|repo_name|>'
Mai 08 18:22:56 home ollama[160182]: print_info: EOG token        = 151664 '<|file_sep|>'
Mai 08 18:22:56 home ollama[160182]: print_info: max token length = 256
Mai 08 18:22:56 home ollama[160182]: llama_model_load: vocab only - skipping tensors
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=DEBUG source=gpu.go:695 msg="no filter required for library cpu"
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=INFO source=server.go:409 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /usr/share/ollama/.ollama/models/blo>
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=DEBUG source=server.go:428 msg=subprocess environment="[PATH=/root/.cargo/bin:/root/.local/bin:/root/bin:/usr/share/Modules/bin:/usr>
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=INFO source=sched.go:450 msg="loaded runners" count=1
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=INFO source=server.go:585 msg="waiting for llama runner to start responding"
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.798+02:00 level=INFO source=server.go:619 msg="waiting for server to become available" status="llm server error"
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.814+02:00 level=INFO source=runner.go:853 msg="starting go runner"
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.814+02:00 level=DEBUG source=ggml.go:93 msg="ggml backend load all from path" path=/usr/local/lib/ollama
Mai 08 18:22:56 home ollama[160182]: load_backend: loaded CPU backend from /usr/local/lib/ollama/libggml-cpu-haswell.so
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.819+02:00 level=INFO source=ggml.go:103 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.L>
Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.819+02:00 level=INFO source=runner.go:913 msg="Server listening on 127.0.0.1:45995"
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: loaded meta data with 31 key-value pairs and 579 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be617>
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   0:                       general.architecture str              = qwen3moe
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   1:                               general.type str              = model
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   2:                               general.name str              = Qwen3 30B A3B
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   3:                           general.basename str              = Qwen3
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   4:                         general.size_label str              = 30B-A3B
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   5:                            general.license str              = apache-2.0
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   6:                       qwen3moe.block_count u32              = 48
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   7:                    qwen3moe.context_length u32              = 40960
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   8:                  qwen3moe.embedding_length u32              = 2048
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv   9:               qwen3moe.feed_forward_length u32              = 6144
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  10:              qwen3moe.attention.head_count u32              = 32
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  11:           qwen3moe.attention.head_count_kv u32              = 4
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  12:                    qwen3moe.rope.freq_base f32              = 1000000.000000
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  13:  qwen3moe.attention.layer_norm_rms_epsilon f32              = 0.000001
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  14:                 qwen3moe.expert_used_count u32              = 8
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  15:              qwen3moe.attention.key_length u32              = 128
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  16:            qwen3moe.attention.value_length u32              = 128
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  17:                      qwen3moe.expert_count u32              = 128
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  18:        qwen3moe.expert_feed_forward_length u32              = 768
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  19:                       tokenizer.ggml.model str              = gpt2
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  20:                         tokenizer.ggml.pre str              = qwen2
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  21:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  22:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  23:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  24:                tokenizer.ggml.eos_token_id u32              = 151645
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  25:            tokenizer.ggml.padding_token_id u32              = 151643
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  26:                tokenizer.ggml.bos_token_id u32              = 151643
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  27:               tokenizer.ggml.add_bos_token bool             = false
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  28:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  29:               general.quantization_version u32              = 2
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv  30:                          general.file_type u32              = 15
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type  f32:  241 tensors
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type  f16:   48 tensors
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q4_K:  265 tensors
Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q6_K:   25 tensors
Mai 08 18:22:56 home ollama[160182]: print_info: file format = GGUF V3 (latest)
Mai 08 18:22:56 home ollama[160182]: print_info: file type   = Q4_K - Medium
Mai 08 18:22:56 home ollama[160182]: print_info: file size   = 17.34 GiB (4.88 BPW)
Mai 08 18:22:57 home ollama[160182]: init_tokenizer: initializing tokenizer for type 2
Mai 08 18:22:57 home ollama[160182]: load: control token: 151660 '<|fim_middle|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151659 '<|fim_prefix|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151653 '<|vision_end|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151648 '<|box_start|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151646 '<|object_ref_start|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151649 '<|box_end|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151655 '<|image_pad|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151651 '<|quad_end|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151647 '<|object_ref_end|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151652 '<|vision_start|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151654 '<|vision_pad|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.049+02:00 level=INFO source=server.go:619 msg="waiting for server to become available" status="llm server loading model"
Mai 08 18:22:57 home ollama[160182]: load: control token: 151656 '<|video_pad|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151644 '<|im_start|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151661 '<|fim_suffix|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: control token: 151650 '<|quad_start|>' is not marked as EOG
Mai 08 18:22:57 home ollama[160182]: load: special tokens cache size = 26
Mai 08 18:22:57 home ollama[160182]: load: token to piece cache size = 0.9311 MB
Mai 08 18:22:57 home ollama[160182]: print_info: arch             = qwen3moe
Mai 08 18:22:57 home ollama[160182]: print_info: vocab_only       = 0
Mai 08 18:22:57 home ollama[160182]: print_info: n_ctx_train      = 40960
Mai 08 18:22:57 home ollama[160182]: print_info: n_embd           = 2048
Mai 08 18:22:57 home ollama[160182]: print_info: n_layer          = 48
Mai 08 18:22:57 home ollama[160182]: print_info: n_head           = 32
Mai 08 18:22:57 home ollama[160182]: print_info: n_head_kv        = 4
Mai 08 18:22:57 home ollama[160182]: print_info: n_rot            = 128
Mai 08 18:22:57 home ollama[160182]: print_info: n_swa            = 0
Mai 08 18:22:57 home ollama[160182]: print_info: n_swa_pattern    = 1
Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_head_k    = 128
Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_head_v    = 128
Mai 08 18:22:57 home ollama[160182]: print_info: n_gqa            = 8
Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_k_gqa     = 512
Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_v_gqa     = 512
Mai 08 18:22:57 home ollama[160182]: print_info: f_norm_eps       = 0.0e+00
Mai 08 18:22:57 home ollama[160182]: print_info: f_norm_rms_eps   = 1.0e-06
Mai 08 18:22:57 home ollama[160182]: print_info: f_clamp_kqv      = 0.0e+00
Mai 08 18:22:57 home ollama[160182]: print_info: f_max_alibi_bias = 0.0e+00
Mai 08 18:22:57 home ollama[160182]: print_info: f_logit_scale    = 0.0e+00
Mai 08 18:22:57 home ollama[160182]: print_info: f_attn_scale     = 0.0e+00
Mai 08 18:22:57 home ollama[160182]: print_info: n_ff             = 6144
Mai 08 18:22:57 home ollama[160182]: print_info: n_expert         = 128
Mai 08 18:22:57 home ollama[160182]: print_info: n_expert_used    = 8
Mai 08 18:22:57 home ollama[160182]: print_info: causal attn      = 1
Mai 08 18:22:57 home ollama[160182]: print_info: pooling type     = 0
Mai 08 18:22:57 home ollama[160182]: print_info: rope type        = 2
Mai 08 18:22:57 home ollama[160182]: print_info: rope scaling     = linear
Mai 08 18:22:57 home ollama[160182]: print_info: freq_base_train  = 1000000.0
Mai 08 18:22:57 home ollama[160182]: print_info: freq_scale_train = 1
Mai 08 18:22:57 home ollama[160182]: print_info: n_ctx_orig_yarn  = 40960
Mai 08 18:22:57 home ollama[160182]: print_info: rope_finetuned   = unknown
Mai 08 18:22:57 home ollama[160182]: print_info: ssm_d_conv       = 0
Mai 08 18:22:57 home ollama[160182]: print_info: ssm_d_inner      = 0
Mai 08 18:22:57 home ollama[160182]: print_info: ssm_d_state      = 0
Mai 08 18:22:57 home ollama[160182]: print_info: ssm_dt_rank      = 0
Mai 08 18:22:57 home ollama[160182]: print_info: ssm_dt_b_c_rms   = 0
Mai 08 18:22:57 home ollama[160182]: print_info: model type       = ?B
Mai 08 18:22:57 home ollama[160182]: print_info: model params     = 30.53 B
Mai 08 18:22:57 home ollama[160182]: print_info: general.name     = Qwen3 30B A3B
Mai 08 18:22:57 home ollama[160182]: print_info: n_ff_exp         = 768
Mai 08 18:22:57 home ollama[160182]: print_info: vocab type       = BPE
Mai 08 18:22:57 home ollama[160182]: print_info: n_vocab          = 151936
Mai 08 18:22:57 home ollama[160182]: print_info: n_merges         = 151387
Mai 08 18:22:57 home ollama[160182]: print_info: BOS token        = 151643 '<|endoftext|>'
Mai 08 18:22:57 home ollama[160182]: print_info: EOS token        = 151645 '<|im_end|>'
Mai 08 18:22:57 home ollama[160182]: print_info: EOT token        = 151645 '<|im_end|>'
Mai 08 18:22:57 home ollama[160182]: print_info: PAD token        = 151643 '<|endoftext|>'
Mai 08 18:22:57 home ollama[160182]: print_info: LF token         = 198 'Ċ'
Mai 08 18:22:57 home ollama[160182]: print_info: FIM PRE token    = 151659 '<|fim_prefix|>'
Mai 08 18:22:57 home ollama[160182]: print_info: FIM SUF token    = 151661 '<|fim_suffix|>'
Mai 08 18:22:57 home ollama[160182]: print_info: FIM MID token    = 151660 '<|fim_middle|>'
Mai 08 18:22:57 home ollama[160182]: print_info: FIM PAD token    = 151662 '<|fim_pad|>'
Mai 08 18:22:57 home ollama[160182]: print_info: FIM REP token    = 151663 '<|repo_name|>'
Mai 08 18:22:57 home ollama[160182]: print_info: FIM SEP token    = 151664 '<|file_sep|>'
Mai 08 18:22:57 home ollama[160182]: print_info: EOG token        = 151643 '<|endoftext|>'
Mai 08 18:22:57 home ollama[160182]: print_info: EOG token        = 151645 '<|im_end|>'
Mai 08 18:22:57 home ollama[160182]: print_info: EOG token        = 151662 '<|fim_pad|>'
Mai 08 18:22:57 home ollama[160182]: print_info: EOG token        = 151663 '<|repo_name|>'
Mai 08 18:22:57 home ollama[160182]: print_info: EOG token        = 151664 '<|file_sep|>'
Mai 08 18:22:57 home ollama[160182]: print_info: max token length = 256
Mai 08 18:22:57 home ollama[160182]: load_tensors: loading model tensors, this can take a while... (mmap = false)
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   0 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   1 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   2 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   3 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   4 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   5 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   6 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   7 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   8 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer   9 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  10 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  11 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  12 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  13 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  14 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  15 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  16 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  17 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  18 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  19 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  20 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  21 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  22 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  23 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  24 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  25 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  26 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  27 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  28 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  29 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  30 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  31 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  32 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  33 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  34 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  35 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  36 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  37 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  38 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  39 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  40 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  41 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  42 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  43 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  44 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  45 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  46 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  47 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors: layer  48 assigned to device CPU, is_swa = 0
Mai 08 18:22:57 home ollama[160182]: load_tensors:          CPU model buffer size = 17754.15 MiB
Mai 08 18:22:57 home ollama[160182]: load_all_data: no device found for buffer type CPU for async uploads
Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.300+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.01"
Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.551+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.04"
Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.801+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.06"
Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.052+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.08"
Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.303+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.10"
Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.554+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.13"
Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.805+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.15"
Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.055+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.17"
Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.306+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.20"
Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.557+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.22"
Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.808+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.25"
Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.059+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.27"
Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.310+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.29"
Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.560+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.31"
Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.811+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.34"
Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.062+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.36"
Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.313+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.39"
Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.564+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.41"
Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.814+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.43"
Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.065+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.45"
Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.316+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.48"
Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.567+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.50"
Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.818+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.52"
Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.069+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.54"
Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.320+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.57"
Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.570+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.59"
Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.821+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.62"
Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.072+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.64"
Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.323+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.66"
Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.574+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.67"
Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.824+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.69"
Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.075+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.72"
Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.326+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.74"
Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.577+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.76"
Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.828+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.78"
Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.078+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.79"
Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.329+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.82"
Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.580+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.84"
Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.831+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.86"
Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.082+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.89"
Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.332+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.91"
Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.583+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.94"
Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.834+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.96"
Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.085+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.98"
Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.336+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.99"
Mai 08 18:23:08 home ollama[160182]: llama_context: constructing llama_context
Mai 08 18:23:08 home ollama[160182]: llama_context: n_seq_max     = 2
Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx         = 8192
Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx_per_seq = 4096
Mai 08 18:23:08 home ollama[160182]: llama_context: n_batch       = 1024
Mai 08 18:23:08 home ollama[160182]: llama_context: n_ubatch      = 512
Mai 08 18:23:08 home ollama[160182]: llama_context: causal_attn   = 1
Mai 08 18:23:08 home ollama[160182]: llama_context: flash_attn    = 0
Mai 08 18:23:08 home ollama[160182]: llama_context: freq_base     = 1000000.0
Mai 08 18:23:08 home ollama[160182]: llama_context: freq_scale    = 1
Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx_per_seq (4096) < n_ctx_train (40960) -- the full capacity of the model will not be utilized
Mai 08 18:23:08 home ollama[160182]: set_abort_callback: call
Mai 08 18:23:08 home ollama[160182]: llama_context:        CPU  output buffer size =     1.17 MiB
Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx = 8192
Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx = 8192 (padded)
Mai 08 18:23:08 home ollama[160182]: init: kv_size = 8192, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 48, can_shift = 1
Mai 08 18:23:08 home ollama[160182]: init: layer   0: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   1: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   2: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   3: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   4: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   5: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   6: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   7: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   8: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer   9: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  10: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  11: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  12: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  13: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  14: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  15: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  16: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  17: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  18: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  19: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  20: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  21: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  22: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  23: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  24: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  25: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  26: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  27: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  28: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  29: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  30: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  31: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  32: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  33: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  34: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  35: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  36: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  37: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  38: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  39: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  40: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  41: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  42: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  43: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  44: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  45: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  46: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: init: layer  47: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU
Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.586+02:00 level=DEBUG source=server.go:630 msg="model load progress 1.00"
Mai 08 18:23:08 home ollama[160182]: init:        CPU KV buffer size =   768.00 MiB
Mai 08 18:23:08 home ollama[160182]: llama_context: KV self size  =  768.00 MiB, K (f16):  384.00 MiB, V (f16):  384.00 MiB
Mai 08 18:23:08 home ollama[160182]: llama_context: enumerating backends
Mai 08 18:23:08 home ollama[160182]: llama_context: backend_ptrs.size() = 1
Mai 08 18:23:08 home ollama[160182]: llama_context: max_nodes = 65536
Mai 08 18:23:08 home ollama[160182]: llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0
Mai 08 18:23:08 home ollama[160182]: llama_context: reserving graph for n_tokens = 512, n_seqs = 1
Mai 08 18:23:08 home ollama[160182]: llama_context: reserving graph for n_tokens = 1, n_seqs = 1
Mai 08 18:23:08 home ollama[160182]: llama_context: reserving graph for n_tokens = 512, n_seqs = 1
Mai 08 18:23:08 home ollama[160182]: llama_context:        CPU compute buffer size =   552.01 MiB
Mai 08 18:23:08 home ollama[160182]: llama_context: graph nodes  = 3126
Mai 08 18:23:08 home ollama[160182]: llama_context: graph splits = 1
Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.837+02:00 level=INFO source=server.go:624 msg="llama runner started in 12.04 seconds"
Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.837+02:00 level=DEBUG source=sched.go:462 msg="finished setting up runner" model=/usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c>
Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.838+02:00 level=DEBUG source=routes.go:1525 msg="chat request" images=0 prompt="<|im_start|>user\nWhat is 1+1?<|im_end|>\n<|im_start|>assistant\n"
Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.840+02:00 level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=0 prompt=15 used=0 remaining=15
Mai 08 18:27:00 home ollama[160182]: [GIN] 2025/05/08 - 18:27:00 | 200 |          4m4s |      172.22.0.2 | POST     "/api/chat"
Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.899+02:00 level=DEBUG source=sched.go:467 msg="context for request finished"
Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.899+02:00 level=DEBUG source=sched.go:341 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models>
Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.899+02:00 level=DEBUG source=sched.go:359 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-e918>
Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.948+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32
Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.949+02:00 level=DEBUG source=sched.go:578 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1>
Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.950+02:00 level=DEBUG source=routes.go:1525 msg="chat request" images=0 prompt="<|im_start|>user\n### Task:\nGenerate a concise, 3-5 word title with>
Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.959+02:00 level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=721 prompt=431 used=3 remaining=428

<details id="__DETAIL_0__"/>
In standard arithmetic using the decimal (base-10) system, **1 + 1 = 2**. This is the most common and straightforward answer. However, the result can vary depending on the context:

1. **Binary (base-2):**  
   $1 + 1 = 10$ (which equals 2 in decimal).

2. **Modular Arithmetic (e.g., modulo 2):**  
   $1 + 1 \equiv 0 \mod 2$.

3. **Real-World Analogies:**  
   In some physical or conceptual scenarios (e.g., combining two drops of water), 1 + 1 might metaphorically represent a single unit, though this is not standard math.

4. **Non-Standard Systems:**  
   In certain abstract mathematical structures (e.g., group theory or set theory), operations might be defined differently, but these are specialized cases.

For most purposes, **1 + 1 = 2** is the correct and widely accepted answer.

Open-WebUI shows the result, but the model seems to continue working in the background. As soon as I stop the docker container of Open-WebUI, the CPU load of ollama goes down.

top

Image

htop

Image

ps ax | grep ollama
 160182 ?        Ssl    0:00 /usr/local/bin/ollama serve
 160299 ?        Sl   239:35 /usr/local/bin/ollama runner --model /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be6176b68c20f156d54a07ac --ctx-size 8192 --batch-size 512 --verbose --threads 12 --no-mmap --parallel 2 --port 45995
<!-- gh-comment-id:2863696876 --> @DRRDietrich commented on GitHub (May 8, 2025): My setup: ```system OS: Fedora Linux 42 (Workstation Edition) x86_64 Kernel: 6.14.5-300.fc42.x86_64 CPU: AMD Ryzen 5 2600X (12) @ 3.800GHz Memory: 30182MiB / 64212MiB ``` The setup is still in testing, so I'm currently the only user. ollama (`0.6.7`) installed on fedora ```bash curl -fsSL https://ollama.com/install.sh | sh ``` `systemctl edit ollama.service` ```config [Service] Environment="OLLAMA_NUM_THREADS=12" Environment="OLLAMA_HOST=0.0.0.0" Environment="OLLAMA_DEBUG=INFO" ``` I'm using `qwen3:30b-a3b` (with additional `PARAMETER num_thread 12`) ```config # Modelfile generated by "ollama show" # To build a new Modelfile based on this, replace FROM with: # FROM qwen3:30b-a3b FROM /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be6176b68c20f156d54a07ac TEMPLATE """{{- if .Messages }} {{- if or .System .Tools }}<|im_start|>system {{- if .System }} {{ .System }} {{- end }} {{- if .Tools }} # Tools You may call one or more functions to assist with the user query. You are provided with function signatures within <tools></tools> XML tags: <tools> {{- range .Tools }} {"type": "function", "function": {{ .Function }}} {{- end }} </tools> For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags: <tool_call> {"name": <function-name>, "arguments": <args-json-object>} </tool_call> {{- end }}<|im_end|> {{ end }} {{- range $i, $_ := .Messages }} {{- $last := eq (len (slice $.Messages $i)) 1 -}} {{- if eq .Role "user" }}<|im_start|>user {{ .Content }}<|im_end|> {{ else if eq .Role "assistant" }}<|im_start|>assistant {{ if .Content }}{{ .Content }} {{- else if .ToolCalls }}<tool_call> {{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}} {{ end }}</tool_call> {{- end }}{{ if not $last }}<|im_end|> {{ end }} {{- else if eq .Role "tool" }}<|im_start|>user <tool_response> {{ .Content }} </tool_response><|im_end|> {{ end }} {{- if and (ne .Role "assistant") $last }}<|im_start|>assistant {{ end }} {{- end }} {{- else }} {{- if .System }}<|im_start|>system {{ .System }}<|im_end|> {{ end }}{{ if .Prompt }}<|im_start|>user {{ .Prompt }}<|im_end|> {{ end }}<|im_start|>assistant {{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}""" PARAMETER repeat_penalty 1 PARAMETER stop <|im_start|> PARAMETER stop <|im_end|> PARAMETER temperature 0.6 PARAMETER top_k 20 PARAMETER top_p 0.95 PARAMETER num_thread 12 LICENSE """ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2024 Alibaba Cloud Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.""" ``` I have no problems as long as I use the model with `ollama run`. As soon as the open-webui is used, the problem described arises. open-webui (`v0.6.7`) as docker container ```yml version: '3' services: openwebui: image: ghcr.io/open-webui/open-webui:main ports: - "3000:8080" volumes: - open-webui:/app/backend/data extra_hosts: - "host.docker.internal:host-gateway" volumes: open-webui: ``` Very rarely, but sometimes there are individual answers according to which the CPU load goes down completely. But in the vast majority of cases, an answer results in the CPU load remaining exactly as high after the reply as it was during the reply generation. Here is an example. Question: `What is 1+1?`. ```log Mai 08 18:22:21 home systemd[1]: Started ollama.service - Ollama Service. Mai 08 18:22:21 home ollama[160182]: 2025/05/08 18:22:21 routes.go:1233: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PR> Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.500+02:00 level=INFO source=images.go:458 msg="total blobs: 5" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.500+02:00 level=INFO source=images.go:465 msg="total unused blobs removed: 0" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.501+02:00 level=INFO source=routes.go:1300 msg="Listening on [::]:11434 (version 0.6.7)" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.501+02:00 level=DEBUG source=sched.go:107 msg="starting llm scheduler" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.501+02:00 level=INFO source=gpu.go:217 msg="looking for compatible GPUs" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.502+02:00 level=DEBUG source=gpu.go:98 msg="searching for GPU discovery libraries for NVIDIA" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.502+02:00 level=DEBUG source=gpu.go:501 msg="Searching for GPU library" name=libcuda.so* Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.502+02:00 level=DEBUG source=gpu.go:525 msg="gpu library search" globs="[/usr/local/lib/ollama/libcuda.so* /libcuda.so* /usr/local/cuda*/targets/*/l> Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.509+02:00 level=DEBUG source=gpu.go:558 msg="discovered GPU libraries" paths=[] Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.509+02:00 level=DEBUG source=gpu.go:501 msg="Searching for GPU library" name=libcudart.so* Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.509+02:00 level=DEBUG source=gpu.go:525 msg="gpu library search" globs="[/usr/local/lib/ollama/libcudart.so* /libcudart.so* /usr/local/lib/ollama/cu> Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.514+02:00 level=DEBUG source=gpu.go:558 msg="discovered GPU libraries" paths="[/usr/local/lib/ollama/cuda_v11/libcudart.so.11.3.109 /usr/local/lib/o> Mai 08 18:22:21 home ollama[160182]: cudaSetDevice err: 35 Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=gpu.go:574 msg="Unable to load cudart library /usr/local/lib/ollama/cuda_v11/libcudart.so.11.3.109: your nvidia driver > Mai 08 18:22:21 home ollama[160182]: cudaSetDevice err: 35 Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=gpu.go:574 msg="Unable to load cudart library /usr/local/lib/ollama/cuda_v12/libcudart.so.12.8.90: your nvidia driver i> Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=WARN source=amd_linux.go:61 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version f> Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:101 msg="evaluating amdgpu node /sys/class/kfd/kfd/topology/nodes/0/properties" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:121 msg="detected CPU /sys/class/kfd/kfd/topology/nodes/0/properties" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:101 msg="evaluating amdgpu node /sys/class/kfd/kfd/topology/nodes/1/properties" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:206 msg="mapping amdgpu to drm sysfs nodes" amdgpu=/sys/class/kfd/kfd/topology/nodes/1/properties vendor=4> Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.515+02:00 level=DEBUG source=amd_linux.go:240 msg=matched amdgpu=/sys/class/kfd/kfd/topology/nodes/1/properties drm=/sys/class/drm/card1/device Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=WARN source=amd_linux.go:309 msg="amdgpu too old gfx803" gpu=0 Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=INFO source=amd_linux.go:402 msg="no compatible amdgpu devices detected" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=INFO source=gpu.go:377 msg="no compatible GPUs were discovered" Mai 08 18:22:21 home ollama[160182]: time=2025-05-08T18:22:21.516+02:00 level=INFO source=types.go:130 msg="inference compute" id=0 library=cpu variant="" compute="" driver=0.0 name="" total="62.7 GiB" availabl> Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.445+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32 Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.446+02:00 level=DEBUG source=gpu.go:391 msg="updating system memory data" before.total="62.7 GiB" before.free="45.5 GiB" before.free_swap="8.0 GiB" > Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.447+02:00 level=DEBUG source=sched.go:183 msg="updating default concurrency" OLLAMA_MAX_LOADED_MODELS=3 gpu_count=1 Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.467+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32 Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.487+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32 Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=DEBUG source=sched.go:213 msg="cpu mode with first model, loading" Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=DEBUG source=gpu.go:391 msg="updating system memory data" before.total="62.7 GiB" before.free="45.0 GiB" before.free_swap="8.0 GiB" > Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=INFO source=server.go:105 msg="system memory" total="62.7 GiB" free="45.0 GiB" free_swap="8.0 GiB" Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=DEBUG source=memory.go:108 msg=evaluating library=cpu gpu_count=1 available="[45.0 GiB]" Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.488+02:00 level=WARN source=ggml.go:152 msg="key not found" key=qwen3moe.vision.block_count default=0 Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.489+02:00 level=INFO source=server.go:138 msg=offload library=cpu layers.requested=-1 layers.model=49 layers.offload=0 layers.split="" memory.availa> Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.489+02:00 level=DEBUG source=server.go:262 msg="compatible gpu libraries" compatible=[] Mai 08 18:22:56 home ollama[160182]: llama_model_loader: loaded meta data with 31 key-value pairs and 579 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be617> Mai 08 18:22:56 home ollama[160182]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 0: general.architecture str = qwen3moe Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 1: general.type str = model Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 2: general.name str = Qwen3 30B A3B Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 3: general.basename str = Qwen3 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 4: general.size_label str = 30B-A3B Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 5: general.license str = apache-2.0 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 6: qwen3moe.block_count u32 = 48 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 7: qwen3moe.context_length u32 = 40960 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 8: qwen3moe.embedding_length u32 = 2048 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 9: qwen3moe.feed_forward_length u32 = 6144 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 10: qwen3moe.attention.head_count u32 = 32 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 11: qwen3moe.attention.head_count_kv u32 = 4 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 12: qwen3moe.rope.freq_base f32 = 1000000.000000 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 13: qwen3moe.attention.layer_norm_rms_epsilon f32 = 0.000001 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 14: qwen3moe.expert_used_count u32 = 8 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 15: qwen3moe.attention.key_length u32 = 128 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 16: qwen3moe.attention.value_length u32 = 128 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 17: qwen3moe.expert_count u32 = 128 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 18: qwen3moe.expert_feed_forward_length u32 = 768 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 19: tokenizer.ggml.model str = gpt2 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 20: tokenizer.ggml.pre str = qwen2 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 21: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 22: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 23: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 24: tokenizer.ggml.eos_token_id u32 = 151645 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 25: tokenizer.ggml.padding_token_id u32 = 151643 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 26: tokenizer.ggml.bos_token_id u32 = 151643 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 27: tokenizer.ggml.add_bos_token bool = false Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 28: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 29: general.quantization_version u32 = 2 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 30: general.file_type u32 = 15 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type f32: 241 tensors Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type f16: 48 tensors Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q4_K: 265 tensors Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q6_K: 25 tensors Mai 08 18:22:56 home ollama[160182]: print_info: file format = GGUF V3 (latest) Mai 08 18:22:56 home ollama[160182]: print_info: file type = Q4_K - Medium Mai 08 18:22:56 home ollama[160182]: print_info: file size = 17.34 GiB (4.88 BPW) Mai 08 18:22:56 home ollama[160182]: init_tokenizer: initializing tokenizer for type 2 Mai 08 18:22:56 home ollama[160182]: load: control token: 151660 '<|fim_middle|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151659 '<|fim_prefix|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151653 '<|vision_end|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151648 '<|box_start|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151646 '<|object_ref_start|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151649 '<|box_end|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151655 '<|image_pad|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151651 '<|quad_end|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151647 '<|object_ref_end|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151652 '<|vision_start|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151654 '<|vision_pad|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151656 '<|video_pad|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151644 '<|im_start|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151661 '<|fim_suffix|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: control token: 151650 '<|quad_start|>' is not marked as EOG Mai 08 18:22:56 home ollama[160182]: load: special tokens cache size = 26 Mai 08 18:22:56 home ollama[160182]: load: token to piece cache size = 0.9311 MB Mai 08 18:22:56 home ollama[160182]: print_info: arch = qwen3moe Mai 08 18:22:56 home ollama[160182]: print_info: vocab_only = 1 Mai 08 18:22:56 home ollama[160182]: print_info: model type = ?B Mai 08 18:22:56 home ollama[160182]: print_info: model params = 30.53 B Mai 08 18:22:56 home ollama[160182]: print_info: general.name = Qwen3 30B A3B Mai 08 18:22:56 home ollama[160182]: print_info: n_ff_exp = 0 Mai 08 18:22:56 home ollama[160182]: print_info: vocab type = BPE Mai 08 18:22:56 home ollama[160182]: print_info: n_vocab = 151936 Mai 08 18:22:56 home ollama[160182]: print_info: n_merges = 151387 Mai 08 18:22:56 home ollama[160182]: print_info: BOS token = 151643 '<|endoftext|>' Mai 08 18:22:56 home ollama[160182]: print_info: EOS token = 151645 '<|im_end|>' Mai 08 18:22:56 home ollama[160182]: print_info: EOT token = 151645 '<|im_end|>' Mai 08 18:22:56 home ollama[160182]: print_info: PAD token = 151643 '<|endoftext|>' Mai 08 18:22:56 home ollama[160182]: print_info: LF token = 198 'Ċ' Mai 08 18:22:56 home ollama[160182]: print_info: FIM PRE token = 151659 '<|fim_prefix|>' Mai 08 18:22:56 home ollama[160182]: print_info: FIM SUF token = 151661 '<|fim_suffix|>' Mai 08 18:22:56 home ollama[160182]: print_info: FIM MID token = 151660 '<|fim_middle|>' Mai 08 18:22:56 home ollama[160182]: print_info: FIM PAD token = 151662 '<|fim_pad|>' Mai 08 18:22:56 home ollama[160182]: print_info: FIM REP token = 151663 '<|repo_name|>' Mai 08 18:22:56 home ollama[160182]: print_info: FIM SEP token = 151664 '<|file_sep|>' Mai 08 18:22:56 home ollama[160182]: print_info: EOG token = 151643 '<|endoftext|>' Mai 08 18:22:56 home ollama[160182]: print_info: EOG token = 151645 '<|im_end|>' Mai 08 18:22:56 home ollama[160182]: print_info: EOG token = 151662 '<|fim_pad|>' Mai 08 18:22:56 home ollama[160182]: print_info: EOG token = 151663 '<|repo_name|>' Mai 08 18:22:56 home ollama[160182]: print_info: EOG token = 151664 '<|file_sep|>' Mai 08 18:22:56 home ollama[160182]: print_info: max token length = 256 Mai 08 18:22:56 home ollama[160182]: llama_model_load: vocab only - skipping tensors Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=DEBUG source=gpu.go:695 msg="no filter required for library cpu" Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=INFO source=server.go:409 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /usr/share/ollama/.ollama/models/blo> Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=DEBUG source=server.go:428 msg=subprocess environment="[PATH=/root/.cargo/bin:/root/.local/bin:/root/bin:/usr/share/Modules/bin:/usr> Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=INFO source=sched.go:450 msg="loaded runners" count=1 Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.797+02:00 level=INFO source=server.go:585 msg="waiting for llama runner to start responding" Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.798+02:00 level=INFO source=server.go:619 msg="waiting for server to become available" status="llm server error" Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.814+02:00 level=INFO source=runner.go:853 msg="starting go runner" Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.814+02:00 level=DEBUG source=ggml.go:93 msg="ggml backend load all from path" path=/usr/local/lib/ollama Mai 08 18:22:56 home ollama[160182]: load_backend: loaded CPU backend from /usr/local/lib/ollama/libggml-cpu-haswell.so Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.819+02:00 level=INFO source=ggml.go:103 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.L> Mai 08 18:22:56 home ollama[160182]: time=2025-05-08T18:22:56.819+02:00 level=INFO source=runner.go:913 msg="Server listening on 127.0.0.1:45995" Mai 08 18:22:56 home ollama[160182]: llama_model_loader: loaded meta data with 31 key-value pairs and 579 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be617> Mai 08 18:22:56 home ollama[160182]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 0: general.architecture str = qwen3moe Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 1: general.type str = model Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 2: general.name str = Qwen3 30B A3B Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 3: general.basename str = Qwen3 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 4: general.size_label str = 30B-A3B Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 5: general.license str = apache-2.0 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 6: qwen3moe.block_count u32 = 48 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 7: qwen3moe.context_length u32 = 40960 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 8: qwen3moe.embedding_length u32 = 2048 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 9: qwen3moe.feed_forward_length u32 = 6144 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 10: qwen3moe.attention.head_count u32 = 32 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 11: qwen3moe.attention.head_count_kv u32 = 4 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 12: qwen3moe.rope.freq_base f32 = 1000000.000000 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 13: qwen3moe.attention.layer_norm_rms_epsilon f32 = 0.000001 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 14: qwen3moe.expert_used_count u32 = 8 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 15: qwen3moe.attention.key_length u32 = 128 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 16: qwen3moe.attention.value_length u32 = 128 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 17: qwen3moe.expert_count u32 = 128 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 18: qwen3moe.expert_feed_forward_length u32 = 768 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 19: tokenizer.ggml.model str = gpt2 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 20: tokenizer.ggml.pre str = qwen2 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 21: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 22: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 23: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 24: tokenizer.ggml.eos_token_id u32 = 151645 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 25: tokenizer.ggml.padding_token_id u32 = 151643 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 26: tokenizer.ggml.bos_token_id u32 = 151643 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 27: tokenizer.ggml.add_bos_token bool = false Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 28: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 29: general.quantization_version u32 = 2 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - kv 30: general.file_type u32 = 15 Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type f32: 241 tensors Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type f16: 48 tensors Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q4_K: 265 tensors Mai 08 18:22:56 home ollama[160182]: llama_model_loader: - type q6_K: 25 tensors Mai 08 18:22:56 home ollama[160182]: print_info: file format = GGUF V3 (latest) Mai 08 18:22:56 home ollama[160182]: print_info: file type = Q4_K - Medium Mai 08 18:22:56 home ollama[160182]: print_info: file size = 17.34 GiB (4.88 BPW) Mai 08 18:22:57 home ollama[160182]: init_tokenizer: initializing tokenizer for type 2 Mai 08 18:22:57 home ollama[160182]: load: control token: 151660 '<|fim_middle|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151659 '<|fim_prefix|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151653 '<|vision_end|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151648 '<|box_start|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151646 '<|object_ref_start|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151649 '<|box_end|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151655 '<|image_pad|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151651 '<|quad_end|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151647 '<|object_ref_end|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151652 '<|vision_start|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151654 '<|vision_pad|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.049+02:00 level=INFO source=server.go:619 msg="waiting for server to become available" status="llm server loading model" Mai 08 18:22:57 home ollama[160182]: load: control token: 151656 '<|video_pad|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151644 '<|im_start|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151661 '<|fim_suffix|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: control token: 151650 '<|quad_start|>' is not marked as EOG Mai 08 18:22:57 home ollama[160182]: load: special tokens cache size = 26 Mai 08 18:22:57 home ollama[160182]: load: token to piece cache size = 0.9311 MB Mai 08 18:22:57 home ollama[160182]: print_info: arch = qwen3moe Mai 08 18:22:57 home ollama[160182]: print_info: vocab_only = 0 Mai 08 18:22:57 home ollama[160182]: print_info: n_ctx_train = 40960 Mai 08 18:22:57 home ollama[160182]: print_info: n_embd = 2048 Mai 08 18:22:57 home ollama[160182]: print_info: n_layer = 48 Mai 08 18:22:57 home ollama[160182]: print_info: n_head = 32 Mai 08 18:22:57 home ollama[160182]: print_info: n_head_kv = 4 Mai 08 18:22:57 home ollama[160182]: print_info: n_rot = 128 Mai 08 18:22:57 home ollama[160182]: print_info: n_swa = 0 Mai 08 18:22:57 home ollama[160182]: print_info: n_swa_pattern = 1 Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_head_k = 128 Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_head_v = 128 Mai 08 18:22:57 home ollama[160182]: print_info: n_gqa = 8 Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_k_gqa = 512 Mai 08 18:22:57 home ollama[160182]: print_info: n_embd_v_gqa = 512 Mai 08 18:22:57 home ollama[160182]: print_info: f_norm_eps = 0.0e+00 Mai 08 18:22:57 home ollama[160182]: print_info: f_norm_rms_eps = 1.0e-06 Mai 08 18:22:57 home ollama[160182]: print_info: f_clamp_kqv = 0.0e+00 Mai 08 18:22:57 home ollama[160182]: print_info: f_max_alibi_bias = 0.0e+00 Mai 08 18:22:57 home ollama[160182]: print_info: f_logit_scale = 0.0e+00 Mai 08 18:22:57 home ollama[160182]: print_info: f_attn_scale = 0.0e+00 Mai 08 18:22:57 home ollama[160182]: print_info: n_ff = 6144 Mai 08 18:22:57 home ollama[160182]: print_info: n_expert = 128 Mai 08 18:22:57 home ollama[160182]: print_info: n_expert_used = 8 Mai 08 18:22:57 home ollama[160182]: print_info: causal attn = 1 Mai 08 18:22:57 home ollama[160182]: print_info: pooling type = 0 Mai 08 18:22:57 home ollama[160182]: print_info: rope type = 2 Mai 08 18:22:57 home ollama[160182]: print_info: rope scaling = linear Mai 08 18:22:57 home ollama[160182]: print_info: freq_base_train = 1000000.0 Mai 08 18:22:57 home ollama[160182]: print_info: freq_scale_train = 1 Mai 08 18:22:57 home ollama[160182]: print_info: n_ctx_orig_yarn = 40960 Mai 08 18:22:57 home ollama[160182]: print_info: rope_finetuned = unknown Mai 08 18:22:57 home ollama[160182]: print_info: ssm_d_conv = 0 Mai 08 18:22:57 home ollama[160182]: print_info: ssm_d_inner = 0 Mai 08 18:22:57 home ollama[160182]: print_info: ssm_d_state = 0 Mai 08 18:22:57 home ollama[160182]: print_info: ssm_dt_rank = 0 Mai 08 18:22:57 home ollama[160182]: print_info: ssm_dt_b_c_rms = 0 Mai 08 18:22:57 home ollama[160182]: print_info: model type = ?B Mai 08 18:22:57 home ollama[160182]: print_info: model params = 30.53 B Mai 08 18:22:57 home ollama[160182]: print_info: general.name = Qwen3 30B A3B Mai 08 18:22:57 home ollama[160182]: print_info: n_ff_exp = 768 Mai 08 18:22:57 home ollama[160182]: print_info: vocab type = BPE Mai 08 18:22:57 home ollama[160182]: print_info: n_vocab = 151936 Mai 08 18:22:57 home ollama[160182]: print_info: n_merges = 151387 Mai 08 18:22:57 home ollama[160182]: print_info: BOS token = 151643 '<|endoftext|>' Mai 08 18:22:57 home ollama[160182]: print_info: EOS token = 151645 '<|im_end|>' Mai 08 18:22:57 home ollama[160182]: print_info: EOT token = 151645 '<|im_end|>' Mai 08 18:22:57 home ollama[160182]: print_info: PAD token = 151643 '<|endoftext|>' Mai 08 18:22:57 home ollama[160182]: print_info: LF token = 198 'Ċ' Mai 08 18:22:57 home ollama[160182]: print_info: FIM PRE token = 151659 '<|fim_prefix|>' Mai 08 18:22:57 home ollama[160182]: print_info: FIM SUF token = 151661 '<|fim_suffix|>' Mai 08 18:22:57 home ollama[160182]: print_info: FIM MID token = 151660 '<|fim_middle|>' Mai 08 18:22:57 home ollama[160182]: print_info: FIM PAD token = 151662 '<|fim_pad|>' Mai 08 18:22:57 home ollama[160182]: print_info: FIM REP token = 151663 '<|repo_name|>' Mai 08 18:22:57 home ollama[160182]: print_info: FIM SEP token = 151664 '<|file_sep|>' Mai 08 18:22:57 home ollama[160182]: print_info: EOG token = 151643 '<|endoftext|>' Mai 08 18:22:57 home ollama[160182]: print_info: EOG token = 151645 '<|im_end|>' Mai 08 18:22:57 home ollama[160182]: print_info: EOG token = 151662 '<|fim_pad|>' Mai 08 18:22:57 home ollama[160182]: print_info: EOG token = 151663 '<|repo_name|>' Mai 08 18:22:57 home ollama[160182]: print_info: EOG token = 151664 '<|file_sep|>' Mai 08 18:22:57 home ollama[160182]: print_info: max token length = 256 Mai 08 18:22:57 home ollama[160182]: load_tensors: loading model tensors, this can take a while... (mmap = false) Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 0 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 1 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 2 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 3 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 4 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 5 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 6 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 7 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 8 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 9 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 10 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 11 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 12 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 13 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 14 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 15 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 16 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 17 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 18 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 19 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 20 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 21 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 22 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 23 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 24 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 25 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 26 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 27 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 28 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 29 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 30 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 31 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 32 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 33 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 34 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 35 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 36 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 37 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 38 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 39 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 40 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 41 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 42 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 43 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 44 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 45 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 46 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 47 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: layer 48 assigned to device CPU, is_swa = 0 Mai 08 18:22:57 home ollama[160182]: load_tensors: CPU model buffer size = 17754.15 MiB Mai 08 18:22:57 home ollama[160182]: load_all_data: no device found for buffer type CPU for async uploads Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.300+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.01" Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.551+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.04" Mai 08 18:22:57 home ollama[160182]: time=2025-05-08T18:22:57.801+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.06" Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.052+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.08" Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.303+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.10" Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.554+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.13" Mai 08 18:22:58 home ollama[160182]: time=2025-05-08T18:22:58.805+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.15" Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.055+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.17" Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.306+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.20" Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.557+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.22" Mai 08 18:22:59 home ollama[160182]: time=2025-05-08T18:22:59.808+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.25" Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.059+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.27" Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.310+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.29" Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.560+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.31" Mai 08 18:23:00 home ollama[160182]: time=2025-05-08T18:23:00.811+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.34" Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.062+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.36" Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.313+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.39" Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.564+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.41" Mai 08 18:23:01 home ollama[160182]: time=2025-05-08T18:23:01.814+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.43" Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.065+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.45" Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.316+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.48" Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.567+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.50" Mai 08 18:23:02 home ollama[160182]: time=2025-05-08T18:23:02.818+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.52" Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.069+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.54" Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.320+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.57" Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.570+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.59" Mai 08 18:23:03 home ollama[160182]: time=2025-05-08T18:23:03.821+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.62" Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.072+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.64" Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.323+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.66" Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.574+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.67" Mai 08 18:23:04 home ollama[160182]: time=2025-05-08T18:23:04.824+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.69" Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.075+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.72" Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.326+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.74" Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.577+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.76" Mai 08 18:23:05 home ollama[160182]: time=2025-05-08T18:23:05.828+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.78" Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.078+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.79" Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.329+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.82" Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.580+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.84" Mai 08 18:23:06 home ollama[160182]: time=2025-05-08T18:23:06.831+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.86" Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.082+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.89" Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.332+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.91" Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.583+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.94" Mai 08 18:23:07 home ollama[160182]: time=2025-05-08T18:23:07.834+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.96" Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.085+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.98" Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.336+02:00 level=DEBUG source=server.go:630 msg="model load progress 0.99" Mai 08 18:23:08 home ollama[160182]: llama_context: constructing llama_context Mai 08 18:23:08 home ollama[160182]: llama_context: n_seq_max = 2 Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx = 8192 Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx_per_seq = 4096 Mai 08 18:23:08 home ollama[160182]: llama_context: n_batch = 1024 Mai 08 18:23:08 home ollama[160182]: llama_context: n_ubatch = 512 Mai 08 18:23:08 home ollama[160182]: llama_context: causal_attn = 1 Mai 08 18:23:08 home ollama[160182]: llama_context: flash_attn = 0 Mai 08 18:23:08 home ollama[160182]: llama_context: freq_base = 1000000.0 Mai 08 18:23:08 home ollama[160182]: llama_context: freq_scale = 1 Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx_per_seq (4096) < n_ctx_train (40960) -- the full capacity of the model will not be utilized Mai 08 18:23:08 home ollama[160182]: set_abort_callback: call Mai 08 18:23:08 home ollama[160182]: llama_context: CPU output buffer size = 1.17 MiB Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx = 8192 Mai 08 18:23:08 home ollama[160182]: llama_context: n_ctx = 8192 (padded) Mai 08 18:23:08 home ollama[160182]: init: kv_size = 8192, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 48, can_shift = 1 Mai 08 18:23:08 home ollama[160182]: init: layer 0: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 1: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 2: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 3: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 4: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 5: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 6: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 7: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 8: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 9: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 10: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 11: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 12: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 13: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 14: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 15: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 16: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 17: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 18: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 19: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 20: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 21: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 22: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 23: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 24: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 25: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 26: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 27: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 28: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 29: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 30: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 31: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 32: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 33: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 34: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 35: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 36: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 37: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 38: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 39: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 40: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 41: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 42: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 43: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 44: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 45: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 46: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: init: layer 47: n_embd_k_gqa = 512, n_embd_v_gqa = 512, dev = CPU Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.586+02:00 level=DEBUG source=server.go:630 msg="model load progress 1.00" Mai 08 18:23:08 home ollama[160182]: init: CPU KV buffer size = 768.00 MiB Mai 08 18:23:08 home ollama[160182]: llama_context: KV self size = 768.00 MiB, K (f16): 384.00 MiB, V (f16): 384.00 MiB Mai 08 18:23:08 home ollama[160182]: llama_context: enumerating backends Mai 08 18:23:08 home ollama[160182]: llama_context: backend_ptrs.size() = 1 Mai 08 18:23:08 home ollama[160182]: llama_context: max_nodes = 65536 Mai 08 18:23:08 home ollama[160182]: llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0 Mai 08 18:23:08 home ollama[160182]: llama_context: reserving graph for n_tokens = 512, n_seqs = 1 Mai 08 18:23:08 home ollama[160182]: llama_context: reserving graph for n_tokens = 1, n_seqs = 1 Mai 08 18:23:08 home ollama[160182]: llama_context: reserving graph for n_tokens = 512, n_seqs = 1 Mai 08 18:23:08 home ollama[160182]: llama_context: CPU compute buffer size = 552.01 MiB Mai 08 18:23:08 home ollama[160182]: llama_context: graph nodes = 3126 Mai 08 18:23:08 home ollama[160182]: llama_context: graph splits = 1 Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.837+02:00 level=INFO source=server.go:624 msg="llama runner started in 12.04 seconds" Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.837+02:00 level=DEBUG source=sched.go:462 msg="finished setting up runner" model=/usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c> Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.838+02:00 level=DEBUG source=routes.go:1525 msg="chat request" images=0 prompt="<|im_start|>user\nWhat is 1+1?<|im_end|>\n<|im_start|>assistant\n" Mai 08 18:23:08 home ollama[160182]: time=2025-05-08T18:23:08.840+02:00 level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=0 prompt=15 used=0 remaining=15 Mai 08 18:27:00 home ollama[160182]: [GIN] 2025/05/08 - 18:27:00 | 200 | 4m4s | 172.22.0.2 | POST "/api/chat" Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.899+02:00 level=DEBUG source=sched.go:467 msg="context for request finished" Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.899+02:00 level=DEBUG source=sched.go:341 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/usr/share/ollama/.ollama/models> Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.899+02:00 level=DEBUG source=sched.go:359 msg="after processing request finished event" modelPath=/usr/share/ollama/.ollama/models/blobs/sha256-e918> Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.948+02:00 level=WARN source=ggml.go:152 msg="key not found" key=general.alignment default=32 Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.949+02:00 level=DEBUG source=sched.go:578 msg="evaluating already loaded" model=/usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1> Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.950+02:00 level=DEBUG source=routes.go:1525 msg="chat request" images=0 prompt="<|im_start|>user\n### Task:\nGenerate a concise, 3-5 word title with> Mai 08 18:27:00 home ollama[160182]: time=2025-05-08T18:27:00.959+02:00 level=DEBUG source=cache.go:104 msg="loading cache slot" id=0 cache=721 prompt=431 used=3 remaining=428 ``` ```log <details id="__DETAIL_0__"/> In standard arithmetic using the decimal (base-10) system, **1 + 1 = 2**. This is the most common and straightforward answer. However, the result can vary depending on the context: 1. **Binary (base-2):** $1 + 1 = 10$ (which equals 2 in decimal). 2. **Modular Arithmetic (e.g., modulo 2):** $1 + 1 \equiv 0 \mod 2$. 3. **Real-World Analogies:** In some physical or conceptual scenarios (e.g., combining two drops of water), 1 + 1 might metaphorically represent a single unit, though this is not standard math. 4. **Non-Standard Systems:** In certain abstract mathematical structures (e.g., group theory or set theory), operations might be defined differently, but these are specialized cases. For most purposes, **1 + 1 = 2** is the correct and widely accepted answer. ``` Open-WebUI shows the result, but the model seems to continue working in the background. As soon as I stop the docker container of Open-WebUI, the CPU load of `ollama` goes down. ```bash top ``` ![Image](https://github.com/user-attachments/assets/d688dd78-ca4a-4632-b8e6-0eacb0fc8ac3) ```bash htop ``` ![Image](https://github.com/user-attachments/assets/be85c1d0-c9a4-448f-82ab-ae255ffb1664) ```bash ps ax | grep ollama 160182 ? Ssl 0:00 /usr/local/bin/ollama serve 160299 ? Sl 239:35 /usr/local/bin/ollama runner --model /usr/share/ollama/.ollama/models/blobs/sha256-e9183b5c18a0cf736578c1e3d1cbd4b7e98e3ad3be6176b68c20f156d54a07ac --ctx-size 8192 --batch-size 512 --verbose --threads 12 --no-mmap --parallel 2 --port 45995 ```
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Reference: github-starred/ollama#63255
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