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[GH-ISSUE #11292] ram fills untill bsod #69506

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opened 2026-05-04 18:16:53 -05:00 by GiteaMirror · 5 comments
GiteaMirror commented 2026-05-04 18:16:53 -05:00
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Originally created by @CStone6 on GitHub (Jul 4, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/11292

What is the issue?

i am coding a app in python to add tool calls i am useing parts of the temple

code

`from ollama import ChatResponse, chat
import ollama
import pyautogui

def type_text(text):
pyautogui.write(text, 1)
return "text written"

def add_two_numbers(a: int, b: int) -> int:
"""
Add two numbers

Args:
a (int): The first number
b (int): The second number

Returns:
int: The sum of the two numbers
"""

return int(a) + int(b)

def subtract_two_numbers(a: int, b: int) -> int:
"""
Subtract two numbers
"""

return int(a) - int(b)

subtract_two_numbers_tool = {
'type': 'function',
'function': {
'name': 'subtract_two_numbers',
'description': 'Subtract two numbers',
'parameters': {
'type': 'object',
'required': ['a', 'b'],
'properties': {
'a': {'type': 'integer', 'description': 'The first number'},
'b': {'type': 'integer', 'description': 'The second number'},
},
},
},
}

type_text_tool = {
"name": "type_text",
"description": "Types the specified text on the user's keyboard.",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to type."
}
},
"required": ["text"]
}
}

messages = [
{
"role": "system",
"content": """
You can use the following tools:

  • add_two_numbers: adds two numbers.
  • subtract_two_numbers: subtracts two numbers.
  • type_text: types text on the user's keyboard.

When using a tool, reply with a tool call and arguments.
"""
},
{'role': 'user', 'content': 'can you use the text typing tool to type hello world?'}
]
print('Prompt:', messages[0]['content'])

available_functions = {
'add_two_numbers': add_two_numbers,
'subtract_two_numbers': subtract_two_numbers,
'type_text': type_text,
}

client = ollama.Client(host="http://192.168.0.147:11434")
response = client.chat(
'llama3.1',
messages=messages,
tools=[add_two_numbers, subtract_two_numbers_tool, type_text_tool],
)

if response.message.tool_calls:

for tool in response.message.tool_calls:

if function_to_call := available_functions.get(tool.function.name):
  print('Calling function:', tool.function.name)
  print('Arguments:', tool.function.arguments)
  output = function_to_call(**tool.function.arguments)
  print('Function output:', output)
else:
  print('Function', tool.function.name, 'not found')

if response.message.tool_calls:

messages.append(response.message)
messages.append({'role': 'tool', 'content': str(output), 'name': tool.function.name})

final_response = client.chat('llama3.1', messages=messages)
print('Final response:', final_response.message.content)

else:
print('No tool calls returned from model')`

Relevant log output

Error: listen tcp 0.0.0.0:11434: bind: Only one usage of each socket address (protocol/network address/port) is normally permitted.
PS C:\Users\tobyc> ollama serve
time=2025-07-03T22:24:37.825-04:00 level=INFO source=routes.go:1235 msg="server config" env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:B:\\ollamamodels OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES:]"
time=2025-07-03T22:24:37.838-04:00 level=INFO source=images.go:476 msg="total blobs: 103"
time=2025-07-03T22:24:37.844-04:00 level=INFO source=images.go:483 msg="total unused blobs removed: 0"
time=2025-07-03T22:24:37.851-04:00 level=INFO source=routes.go:1288 msg="Listening on [::]:11434 (version 0.9.5)"
time=2025-07-03T22:24:37.851-04:00 level=INFO source=gpu.go:217 msg="looking for compatible GPUs"
time=2025-07-03T22:24:37.851-04:00 level=INFO source=gpu_windows.go:167 msg=packages count=1
time=2025-07-03T22:24:37.851-04:00 level=INFO source=gpu_windows.go:214 msg="" package=0 cores=8 efficiency=0 threads=16
time=2025-07-03T22:24:37.998-04:00 level=INFO source=types.go:130 msg="inference compute" id=GPU-b2b23809-88e2-b26d-6ac7-07229e0d1ff7 library=cuda variant=v12 compute=8.9 driver=12.9 name="NVIDIA GeForce RTX 4060 Ti" total="16.0 GiB" available="14.9 GiB"
time=2025-07-03T22:25:04.118-04:00 level=INFO source=sched.go:788 msg="new model will fit in available VRAM in single GPU, loading" model=B:\ollamamodels\blobs\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 gpu=GPU-b2b23809-88e2-b26d-6ac7-07229e0d1ff7 parallel=2 available=14720888832 required="6.5 GiB"
time=2025-07-03T22:25:04.136-04:00 level=INFO source=server.go:135 msg="system memory" total="63.9 GiB" free="46.8 GiB" free_swap="45.7 GiB"
time=2025-07-03T22:25:04.136-04:00 level=INFO source=server.go:175 msg=offload library=cuda layers.requested=-1 layers.model=33 layers.offload=33 layers.split="" memory.available="[13.7 GiB]" memory.gpu_overhead="0 B" memory.required.full="6.5 GiB" memory.required.partial="6.5 GiB" memory.required.kv="1.0 GiB" memory.required.allocations="[6.5 GiB]" memory.weights.total="4.3 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="560.0 MiB" memory.graph.partial="677.5 MiB"
llama_model_loader: loaded meta data with 29 key-value pairs and 292 tensors from B:\ollamamodels\blobs\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Meta Llama 3.1 8B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Meta-Llama-3.1
llama_model_loader: - kv   5:                         general.size_label str              = 8B
llama_model_loader: - kv   6:                            general.license str              = llama3.1
llama_model_loader: - kv   7:                               general.tags arr[str,6]       = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv   8:                          general.languages arr[str,8]       = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv   9:                          llama.block_count u32              = 32
llama_model_loader: - kv  10:                       llama.context_length u32              = 131072
llama_model_loader: - kv  11:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv  12:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv  13:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv  14:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  15:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  16:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  17:                          general.file_type u32              = 15
llama_model_loader: - kv  18:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  24:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  25:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  26:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  27:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv  28:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   66 tensors
llama_model_loader: - type q4_K:  193 tensors
llama_model_loader: - type q6_K:   33 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 4.58 GiB (4.89 BPW)
load: special tokens cache size = 256
load: token to piece cache size = 0.7999 MB
print_info: arch             = llama
print_info: vocab_only       = 1
print_info: model type       = ?B
print_info: model params     = 8.03 B
print_info: general.name     = Meta Llama 3.1 8B Instruct
print_info: vocab type       = BPE
print_info: n_vocab          = 128256
print_info: n_merges         = 280147
print_info: BOS token        = 128000 '<|begin_of_text|>'
print_info: EOS token        = 128009 '<|eot_id|>'
print_info: EOT token        = 128009 '<|eot_id|>'
print_info: EOM token        = 128008 '<|eom_id|>'
print_info: LF token         = 198 'Ċ'
print_info: EOG token        = 128008 '<|eom_id|>'
print_info: EOG token        = 128009 '<|eot_id|>'
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2025-07-03T22:25:04.387-04:00 level=INFO source=server.go:438 msg="starting llama server" cmd="C:\\Users\\tobyc\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model B:\\ollamamodels\\blobs\\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 --ctx-size 8192 --batch-size 512 --n-gpu-layers 33 --threads 8 --no-mmap --parallel 2 --port 53940"
time=2025-07-03T22:25:04.392-04:00 level=INFO source=sched.go:483 msg="loaded runners" count=1
time=2025-07-03T22:25:04.392-04:00 level=INFO source=server.go:598 msg="waiting for llama runner to start responding"
time=2025-07-03T22:25:04.393-04:00 level=INFO source=server.go:632 msg="waiting for server to become available" status="llm server error"
time=2025-07-03T22:25:04.437-04:00 level=INFO source=runner.go:815 msg="starting go runner"
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4060 Ti, compute capability 8.9, VMM: yes
load_backend: loaded CUDA backend from C:\Users\tobyc\AppData\Local\Programs\Ollama\lib\ollama\ggml-cuda.dll
load_backend: loaded CPU backend from C:\Users\tobyc\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-haswell.dll
time=2025-07-03T22:25:04.557-04:00 level=INFO source=ggml.go:104 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.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=500,600,610,700,750,800,860,870,890,900,1200 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang)
time=2025-07-03T22:25:04.557-04:00 level=INFO source=runner.go:874 msg="Server listening on 127.0.0.1:53940"
llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 4060 Ti) - 15225 MiB free
time=2025-07-03T22:25:04.643-04:00 level=INFO source=server.go:632 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: loaded meta data with 29 key-value pairs and 292 tensors from B:\ollamamodels\blobs\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Meta Llama 3.1 8B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Meta-Llama-3.1
llama_model_loader: - kv   5:                         general.size_label str              = 8B
llama_model_loader: - kv   6:                            general.license str              = llama3.1
llama_model_loader: - kv   7:                               general.tags arr[str,6]       = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv   8:                          general.languages arr[str,8]       = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv   9:                          llama.block_count u32              = 32
llama_model_loader: - kv  10:                       llama.context_length u32              = 131072
llama_model_loader: - kv  11:                     llama.embedding_length u32              = 4096
llama_model_loader: - kv  12:                  llama.feed_forward_length u32              = 14336
llama_model_loader: - kv  13:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv  14:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  15:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  16:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  17:                          general.file_type u32              = 15
llama_model_loader: - kv  18:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 128
llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  24:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  25:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  26:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  27:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv  28:               general.quantization_version u32              = 2
llama_model_loader: - type  f32:   66 tensors
llama_model_loader: - type q4_K:  193 tensors
llama_model_loader: - type q6_K:   33 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 4.58 GiB (4.89 BPW)
load: special tokens cache size = 256
load: token to piece cache size = 0.7999 MB
print_info: arch             = llama
print_info: vocab_only       = 0
print_info: n_ctx_train      = 131072
print_info: n_embd           = 4096
print_info: n_layer          = 32
print_info: n_head           = 32
print_info: n_head_kv        = 8
print_info: n_rot            = 128
print_info: n_swa            = 0
print_info: n_swa_pattern    = 1
print_info: n_embd_head_k    = 128
print_info: n_embd_head_v    = 128
print_info: n_gqa            = 4
print_info: n_embd_k_gqa     = 1024
print_info: n_embd_v_gqa     = 1024
print_info: f_norm_eps       = 0.0e+00
print_info: f_norm_rms_eps   = 1.0e-05
print_info: f_clamp_kqv      = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale    = 0.0e+00
print_info: f_attn_scale     = 0.0e+00
print_info: n_ff             = 14336
print_info: n_expert         = 0
print_info: n_expert_used    = 0
print_info: causal attn      = 1
print_info: pooling type     = 0
print_info: rope type        = 0
print_info: rope scaling     = linear
print_info: freq_base_train  = 500000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn  = 131072
print_info: rope_finetuned   = unknown
print_info: ssm_d_conv       = 0
print_info: ssm_d_inner      = 0
print_info: ssm_d_state      = 0
print_info: ssm_dt_rank      = 0
print_info: ssm_dt_b_c_rms   = 0
print_info: model type       = 8B
print_info: model params     = 8.03 B
print_info: general.name     = Meta Llama 3.1 8B Instruct
print_info: vocab type       = BPE
print_info: n_vocab          = 128256
print_info: n_merges         = 280147
print_info: BOS token        = 128000 '<|begin_of_text|>'
print_info: EOS token        = 128009 '<|eot_id|>'
print_info: EOT token        = 128009 '<|eot_id|>'
print_info: EOM token        = 128008 '<|eom_id|>'
print_info: LF token         = 198 'Ċ'
print_info: EOG token        = 128008 '<|eom_id|>'
print_info: EOG token        = 128009 '<|eot_id|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading 32 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 33/33 layers to GPU
load_tensors:        CUDA0 model buffer size =  4403.49 MiB
load_tensors:          CPU model buffer size =   281.81 MiB
llama_context: constructing llama_context
llama_context: n_seq_max     = 2
llama_context: n_ctx         = 8192
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch       = 1024
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = 0
llama_context: freq_base     = 500000.0
llama_context: freq_scale    = 1
llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_context:  CUDA_Host  output buffer size =     1.01 MiB
llama_kv_cache_unified: kv_size = 8192, type_k = 'f16', type_v = 'f16', n_layer = 32, can_shift = 1, padding = 32
llama_kv_cache_unified:      CUDA0 KV buffer size =  1024.00 MiB
llama_kv_cache_unified: KV self size  = 1024.00 MiB, K (f16):  512.00 MiB, V (f16):  512.00 MiB
llama_context:      CUDA0 compute buffer size =   560.00 MiB
llama_context:  CUDA_Host compute buffer size =    24.01 MiB
llama_context: graph nodes  = 1094
llama_context: graph splits = 2
time=2025-07-03T22:25:05.895-04:00 level=INFO source=server.go:637 msg="llama runner started in 1.50 seconds"
[GIN] 2025/07/03 - 22:26:14 | 200 |       525.9µs |       127.0.0.1 | HEAD     "/"
[GIN] 2025/07/03 - 22:26:14 | 200 |            0s |       127.0.0.1 | GET      "/api/ps"

OS

Windows

GPU

Nvidia

CPU

AMD

Ollama version

0.9.5

Originally created by @CStone6 on GitHub (Jul 4, 2025). Original GitHub issue: https://github.com/ollama/ollama/issues/11292 ### What is the issue? i am coding a app in python to add tool calls i am useing parts of the temple code `from ollama import ChatResponse, chat import ollama import pyautogui def type_text(text): pyautogui.write(text, 1) return "text written" def add_two_numbers(a: int, b: int) -> int: """ Add two numbers Args: a (int): The first number b (int): The second number Returns: int: The sum of the two numbers """ return int(a) + int(b) def subtract_two_numbers(a: int, b: int) -> int: """ Subtract two numbers """ return int(a) - int(b) subtract_two_numbers_tool = { 'type': 'function', 'function': { 'name': 'subtract_two_numbers', 'description': 'Subtract two numbers', 'parameters': { 'type': 'object', 'required': ['a', 'b'], 'properties': { 'a': {'type': 'integer', 'description': 'The first number'}, 'b': {'type': 'integer', 'description': 'The second number'}, }, }, }, } type_text_tool = { "name": "type_text", "description": "Types the specified text on the user's keyboard.", "parameters": { "type": "object", "properties": { "text": { "type": "string", "description": "The text to type." } }, "required": ["text"] } } messages = [ { "role": "system", "content": """ You can use the following tools: - add_two_numbers: adds two numbers. - subtract_two_numbers: subtracts two numbers. - type_text: types text on the user's keyboard. When using a tool, reply with a tool call and arguments. """ }, {'role': 'user', 'content': 'can you use the text typing tool to type hello world?'} ] print('Prompt:', messages[0]['content']) available_functions = { 'add_two_numbers': add_two_numbers, 'subtract_two_numbers': subtract_two_numbers, 'type_text': type_text, } client = ollama.Client(host="http://192.168.0.147:11434") response = client.chat( 'llama3.1', messages=messages, tools=[add_two_numbers, subtract_two_numbers_tool, type_text_tool], ) if response.message.tool_calls: for tool in response.message.tool_calls: if function_to_call := available_functions.get(tool.function.name): print('Calling function:', tool.function.name) print('Arguments:', tool.function.arguments) output = function_to_call(**tool.function.arguments) print('Function output:', output) else: print('Function', tool.function.name, 'not found') if response.message.tool_calls: messages.append(response.message) messages.append({'role': 'tool', 'content': str(output), 'name': tool.function.name}) final_response = client.chat('llama3.1', messages=messages) print('Final response:', final_response.message.content) else: print('No tool calls returned from model')` ### Relevant log output ```shell Error: listen tcp 0.0.0.0:11434: bind: Only one usage of each socket address (protocol/network address/port) is normally permitted. PS C:\Users\tobyc> ollama serve time=2025-07-03T22:24:37.825-04:00 level=INFO source=routes.go:1235 msg="server config" env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:4096 OLLAMA_DEBUG:INFO OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:B:\\ollamamodels OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES:]" time=2025-07-03T22:24:37.838-04:00 level=INFO source=images.go:476 msg="total blobs: 103" time=2025-07-03T22:24:37.844-04:00 level=INFO source=images.go:483 msg="total unused blobs removed: 0" time=2025-07-03T22:24:37.851-04:00 level=INFO source=routes.go:1288 msg="Listening on [::]:11434 (version 0.9.5)" time=2025-07-03T22:24:37.851-04:00 level=INFO source=gpu.go:217 msg="looking for compatible GPUs" time=2025-07-03T22:24:37.851-04:00 level=INFO source=gpu_windows.go:167 msg=packages count=1 time=2025-07-03T22:24:37.851-04:00 level=INFO source=gpu_windows.go:214 msg="" package=0 cores=8 efficiency=0 threads=16 time=2025-07-03T22:24:37.998-04:00 level=INFO source=types.go:130 msg="inference compute" id=GPU-b2b23809-88e2-b26d-6ac7-07229e0d1ff7 library=cuda variant=v12 compute=8.9 driver=12.9 name="NVIDIA GeForce RTX 4060 Ti" total="16.0 GiB" available="14.9 GiB" time=2025-07-03T22:25:04.118-04:00 level=INFO source=sched.go:788 msg="new model will fit in available VRAM in single GPU, loading" model=B:\ollamamodels\blobs\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 gpu=GPU-b2b23809-88e2-b26d-6ac7-07229e0d1ff7 parallel=2 available=14720888832 required="6.5 GiB" time=2025-07-03T22:25:04.136-04:00 level=INFO source=server.go:135 msg="system memory" total="63.9 GiB" free="46.8 GiB" free_swap="45.7 GiB" time=2025-07-03T22:25:04.136-04:00 level=INFO source=server.go:175 msg=offload library=cuda layers.requested=-1 layers.model=33 layers.offload=33 layers.split="" memory.available="[13.7 GiB]" memory.gpu_overhead="0 B" memory.required.full="6.5 GiB" memory.required.partial="6.5 GiB" memory.required.kv="1.0 GiB" memory.required.allocations="[6.5 GiB]" memory.weights.total="4.3 GiB" memory.weights.repeating="3.9 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="560.0 MiB" memory.graph.partial="677.5 MiB" llama_model_loader: loaded meta data with 29 key-value pairs and 292 tensors from B:\ollamamodels\blobs\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 8B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1 llama_model_loader: - kv 5: general.size_label str = 8B llama_model_loader: - kv 6: general.license str = llama3.1 llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam... llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ... llama_model_loader: - kv 9: llama.block_count u32 = 32 llama_model_loader: - kv 10: llama.context_length u32 = 131072 llama_model_loader: - kv 11: llama.embedding_length u32 = 4096 llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 13: llama.attention.head_count u32 = 32 llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 17: general.file_type u32 = 15 llama_model_loader: - kv 18: llama.vocab_size u32 = 128256 llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ... llama_model_loader: - kv 28: general.quantization_version u32 = 2 llama_model_loader: - type f32: 66 tensors llama_model_loader: - type q4_K: 193 tensors llama_model_loader: - type q6_K: 33 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 4.58 GiB (4.89 BPW) load: special tokens cache size = 256 load: token to piece cache size = 0.7999 MB print_info: arch = llama print_info: vocab_only = 1 print_info: model type = ?B print_info: model params = 8.03 B print_info: general.name = Meta Llama 3.1 8B Instruct print_info: vocab type = BPE print_info: n_vocab = 128256 print_info: n_merges = 280147 print_info: BOS token = 128000 '<|begin_of_text|>' print_info: EOS token = 128009 '<|eot_id|>' print_info: EOT token = 128009 '<|eot_id|>' print_info: EOM token = 128008 '<|eom_id|>' print_info: LF token = 198 'Ċ' print_info: EOG token = 128008 '<|eom_id|>' print_info: EOG token = 128009 '<|eot_id|>' print_info: max token length = 256 llama_model_load: vocab only - skipping tensors time=2025-07-03T22:25:04.387-04:00 level=INFO source=server.go:438 msg="starting llama server" cmd="C:\\Users\\tobyc\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model B:\\ollamamodels\\blobs\\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 --ctx-size 8192 --batch-size 512 --n-gpu-layers 33 --threads 8 --no-mmap --parallel 2 --port 53940" time=2025-07-03T22:25:04.392-04:00 level=INFO source=sched.go:483 msg="loaded runners" count=1 time=2025-07-03T22:25:04.392-04:00 level=INFO source=server.go:598 msg="waiting for llama runner to start responding" time=2025-07-03T22:25:04.393-04:00 level=INFO source=server.go:632 msg="waiting for server to become available" status="llm server error" time=2025-07-03T22:25:04.437-04:00 level=INFO source=runner.go:815 msg="starting go runner" ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 4060 Ti, compute capability 8.9, VMM: yes load_backend: loaded CUDA backend from C:\Users\tobyc\AppData\Local\Programs\Ollama\lib\ollama\ggml-cuda.dll load_backend: loaded CPU backend from C:\Users\tobyc\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-haswell.dll time=2025-07-03T22:25:04.557-04:00 level=INFO source=ggml.go:104 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.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=500,600,610,700,750,800,860,870,890,900,1200 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang) time=2025-07-03T22:25:04.557-04:00 level=INFO source=runner.go:874 msg="Server listening on 127.0.0.1:53940" llama_model_load_from_file_impl: using device CUDA0 (NVIDIA GeForce RTX 4060 Ti) - 15225 MiB free time=2025-07-03T22:25:04.643-04:00 level=INFO source=server.go:632 msg="waiting for server to become available" status="llm server loading model" llama_model_loader: loaded meta data with 29 key-value pairs and 292 tensors from B:\ollamamodels\blobs\sha256-667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29 (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 8B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1 llama_model_loader: - kv 5: general.size_label str = 8B llama_model_loader: - kv 6: general.license str = llama3.1 llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam... llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ... llama_model_loader: - kv 9: llama.block_count u32 = 32 llama_model_loader: - kv 10: llama.context_length u32 = 131072 llama_model_loader: - kv 11: llama.embedding_length u32 = 4096 llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 13: llama.attention.head_count u32 = 32 llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000 llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 17: general.file_type u32 = 15 llama_model_loader: - kv 18: llama.vocab_size u32 = 128256 llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000 llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009 llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ... llama_model_loader: - kv 28: general.quantization_version u32 = 2 llama_model_loader: - type f32: 66 tensors llama_model_loader: - type q4_K: 193 tensors llama_model_loader: - type q6_K: 33 tensors print_info: file format = GGUF V3 (latest) print_info: file type = Q4_K - Medium print_info: file size = 4.58 GiB (4.89 BPW) load: special tokens cache size = 256 load: token to piece cache size = 0.7999 MB print_info: arch = llama print_info: vocab_only = 0 print_info: n_ctx_train = 131072 print_info: n_embd = 4096 print_info: n_layer = 32 print_info: n_head = 32 print_info: n_head_kv = 8 print_info: n_rot = 128 print_info: n_swa = 0 print_info: n_swa_pattern = 1 print_info: n_embd_head_k = 128 print_info: n_embd_head_v = 128 print_info: n_gqa = 4 print_info: n_embd_k_gqa = 1024 print_info: n_embd_v_gqa = 1024 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-05 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 0.0e+00 print_info: f_attn_scale = 0.0e+00 print_info: n_ff = 14336 print_info: n_expert = 0 print_info: n_expert_used = 0 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 0 print_info: rope scaling = linear print_info: freq_base_train = 500000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 131072 print_info: rope_finetuned = unknown print_info: ssm_d_conv = 0 print_info: ssm_d_inner = 0 print_info: ssm_d_state = 0 print_info: ssm_dt_rank = 0 print_info: ssm_dt_b_c_rms = 0 print_info: model type = 8B print_info: model params = 8.03 B print_info: general.name = Meta Llama 3.1 8B Instruct print_info: vocab type = BPE print_info: n_vocab = 128256 print_info: n_merges = 280147 print_info: BOS token = 128000 '<|begin_of_text|>' print_info: EOS token = 128009 '<|eot_id|>' print_info: EOT token = 128009 '<|eot_id|>' print_info: EOM token = 128008 '<|eom_id|>' print_info: LF token = 198 'Ċ' print_info: EOG token = 128008 '<|eom_id|>' print_info: EOG token = 128009 '<|eot_id|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = false) load_tensors: offloading 32 repeating layers to GPU load_tensors: offloading output layer to GPU load_tensors: offloaded 33/33 layers to GPU load_tensors: CUDA0 model buffer size = 4403.49 MiB load_tensors: CPU model buffer size = 281.81 MiB llama_context: constructing llama_context llama_context: n_seq_max = 2 llama_context: n_ctx = 8192 llama_context: n_ctx_per_seq = 4096 llama_context: n_batch = 1024 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = 0 llama_context: freq_base = 500000.0 llama_context: freq_scale = 1 llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized llama_context: CUDA_Host output buffer size = 1.01 MiB llama_kv_cache_unified: kv_size = 8192, type_k = 'f16', type_v = 'f16', n_layer = 32, can_shift = 1, padding = 32 llama_kv_cache_unified: CUDA0 KV buffer size = 1024.00 MiB llama_kv_cache_unified: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB llama_context: CUDA0 compute buffer size = 560.00 MiB llama_context: CUDA_Host compute buffer size = 24.01 MiB llama_context: graph nodes = 1094 llama_context: graph splits = 2 time=2025-07-03T22:25:05.895-04:00 level=INFO source=server.go:637 msg="llama runner started in 1.50 seconds" [GIN] 2025/07/03 - 22:26:14 | 200 | 525.9µs | 127.0.0.1 | HEAD "/" [GIN] 2025/07/03 - 22:26:14 | 200 | 0s | 127.0.0.1 | GET "/api/ps" ``` ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.9.5
GiteaMirror added the bug label 2026-05-04 18:16:54 -05:00
GiteaMirror commented 2026-05-04 18:16:56 -05:00
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@Notbici commented on GitHub (Jul 4, 2025):

BSOD would be something kernel related no? So bad driver or faulty hardware?

I wouldn't write off Ollama could set something off, but doing a memory test (setup a usb with memtest64) and verify your ram isn't erroring.

<!-- gh-comment-id:3034677802 --> @Notbici commented on GitHub (Jul 4, 2025): BSOD would be something kernel related no? So bad driver or faulty hardware? I wouldn't write off Ollama could set something off, but doing a memory test (setup a usb with memtest64) and verify your ram isn't erroring.
GiteaMirror commented 2026-05-04 18:16:56 -05:00
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@CStone6 commented on GitHub (Jul 4, 2025):

BSOD would be something kernel related no? So bad driver or faulty hardware?

I wouldn't write off Ollama could set something off, but doing a memory test (setup a usb with memtest64) and verify your ram isn't erroring.

i will do a memory test but i am runing llama3.1:8b and that fits in to my 16gb of vram and i dont think it take more then 50gb of ram

<!-- gh-comment-id:3036518833 --> @CStone6 commented on GitHub (Jul 4, 2025): > BSOD would be something kernel related no? So bad driver or faulty hardware? > > I wouldn't write off Ollama could set something off, but doing a memory test (setup a usb with memtest64) and verify your ram isn't erroring. i will do a memory test but i am runing llama3.1:8b and that fits in to my 16gb of vram and i dont think it take more then 50gb of ram
GiteaMirror commented 2026-05-04 18:16:57 -05:00
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@CStone6 commented on GitHub (Jul 5, 2025):

Also my 2 ram sticks failed so I will check if it works tomorrow

<!-- gh-comment-id:3038113294 --> @CStone6 commented on GitHub (Jul 5, 2025): Also my 2 ram sticks failed so I will check if it works tomorrow
GiteaMirror commented 2026-05-04 18:16:59 -05:00
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@dhiltgen commented on GitHub (Jul 5, 2025):

Sounds like it was a hardware fault.

<!-- gh-comment-id:3040353687 --> @dhiltgen commented on GitHub (Jul 5, 2025): Sounds like it was a hardware fault.
GiteaMirror commented 2026-05-04 18:17:00 -05:00
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@CStone6 commented on GitHub (Jul 5, 2025):

Sounds like it was a hardware fault.

with the ram removed my ram still fills up and nothing happens

Image

with terminal it runs fine

Image
<!-- gh-comment-id:3040459372 --> @CStone6 commented on GitHub (Jul 5, 2025): > Sounds like it was a hardware fault. with the ram removed my ram still fills up and nothing happens <img width="1916" height="1024" alt="Image" src="https://github.com/user-attachments/assets/951be9ca-4a03-499f-a6cc-47079a315ac6" /> with terminal it runs fine <img width="1096" height="589" alt="Image" src="https://github.com/user-attachments/assets/2fd27d10-3cd9-44fa-8b56-c34f68fdf3ec" />
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Reference: github-starred/ollama#69506
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