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parth-mlx-decode-checkpoints
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Originally created by @lowlyocean on GitHub (Apr 7, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/10167
What is the issue?
FROM mistral-small3.1:24b-instruct-2503-fp16ollama create -q q2_k mistral-small3.1:24b-instruct-2503-q2_kollama psLog keeps saying there is not enough VRAM to allocate any layers, but the entire quantized model is only 10GB
Relevant log output
OS
Windows
GPU
Nvidia
CPU
Intel
Ollama version
0.6.5
@btibor91 commented on GitHub (Apr 7, 2025):
I am experiencing the same problem with
mistral-small3.1:24b(24b-instruct-2503-q4_K_M) - size is 15 GB, and with 20 GB of VRAM, it gets split into 8 GB and 7 GB between VRAM and RAM.ollama 0.6.5 / Ubuntu 22.04
ollama psshows size 26 GB, butollama lsonly 15 GBNVIDIA RTX 4000 SFF Ada (20GB VRAM)
@rick-github commented on GitHub (Apr 7, 2025):
It looks like ollama is wildly over-estimating the VRAM required.
nvidia-smishows the backend only allocated 5.3G where ollama estimated 15.4G.@btibor91 commented on GitHub (Apr 7, 2025):
Possibly related to #10128
@rick-github commented on GitHub (Apr 7, 2025):
No flash attention so not directly related to #10128. But ollama has always had issues with correct estimations, it's just gotten worse with the new go-based runner - gemma3 has the same problem (#9791, #10040)
@jessegross commented on GitHub (Apr 7, 2025):
https://github.com/ollama/ollama/issues/9791#issuecomment-2755958292
@lowlyocean commented on GitHub (Apr 7, 2025):
Is this a regression worthy of a rollback until the issue with the new runner gets sorted out?
It seems quite severe for a 10GB model to have no layers at all allocated to 15GB available VRAM
@maxi1134 commented on GitHub (Apr 7, 2025):
Anyone knows how i can force the offload?
I tried setting num_gpu to a wildly large number ( 170) for mistral small 3.1 in attempts to get it to run, but it only offloads 16gb out of the 24 available on my 3090.
@rick-github commented on GitHub (Apr 7, 2025):
16G is the full model, it's fully offloaded.
@lowlyocean commented on GitHub (Apr 7, 2025):
I just tried setting
PARAMETER num_gpu 32by adding to the Modelfile dumped from the Q2_K quant and regenerating the model from it. Still seeing 100% CPU use@rick-github commented on GitHub (Apr 7, 2025):
I suspect you are hitting a corner case. Normally, ollama will compute that at least one layer will fit, and will list the GPU backends in the list of backends to consider when the runner loads the model. This is where you can override it by setting
num_gpu, the runner loads a GPU backend and thennum_gpukicks in. In your case, ollama has decided that no way will it be able to load a layer into the GPU, so the GPU backends are not included in the list of backends to choose from. You can hack around this by adding the path to the GPU library directory to the PATH environment variable in the server.@rick-github commented on GitHub (Apr 7, 2025):
Although, having said that, I see that you have 9.9G available on one of your cards. I think that ollama should be able to load at least one layer there, so maybe my guess above is incorrect. If you can supply server logs it may be easier to diagnose.
@lowlyocean commented on GitHub (Apr 7, 2025):
Any part of the log that can confirm if this is happening? Because I have other (larger) models than this 10GB quant which get loaded fully onto the GPUs. Even with this latest release (0.6.5) of ollama. So that also rules out failing to find the GPU libraries.
Could the quantization to Q2_K somehow be making the model a single massive layer?
@maxi1134 commented on GitHub (Apr 7, 2025):
Odd, it still shows some CPU usage in
ollama ps@lowlyocean commented on GitHub (Apr 7, 2025):
@rick-github commented on GitHub (Apr 8, 2025):
@maxi1134
The output from
ollama psis calculated before the value ofnum_gpuis taken into account, so is incorrect.@rick-github commented on GitHub (Apr 8, 2025):
@lowlyocean
Yep, decided that it couldn't fit a single layer anywhere. It might have something to do with the huge projector graph. it basically edges everything out. You should be able to get around that by adding
%LocalAppData%\Programs\Ollama\lib\cuda_v12to PATH in the server environment.@wbste commented on GitHub (Apr 8, 2025):
Same issue on my 3090, I can't get the full thing to load into VRAM. Latest Ollama, on Windows. Not using flash attn. Other 27B and 32B parameters models work fine 100% offloaded. Below says it needs 24.5 GB to do so, which seems high for a
Q4_K_Mquant?Here's gemma3:27b for reference, 100% GPU offloading, same quant:
@rick-github commented on GitHub (Apr 8, 2025):
@lowlyocean Can you add logs from the most recent run?
@lowlyocean commented on GitHub (Apr 8, 2025):
@rick-github commented on GitHub (Apr 8, 2025):
It found the GPU backend but
num_gpuwas unset, so it loaded the number of layers it calculated that would fit, ie zero.@lowlyocean commented on GitHub (Apr 8, 2025):
With the default context window of 2048, I was able to force num_gpu to 41 (all the layers) and it seems to load onto the GPU (despite ollama ps showing 100% CPU).
However, increasing context window to 8192 causes a crash:
Is there already someone tackling the underlying issue causing the need to have to set num_gpu and manipulate PATH manually?
@arturo-air commented on GitHub (Apr 8, 2025):
Something similar is happening to me. My ollama version is 0.6.5, and I just pulled the mistral model
ollama run mistral-small3.1(hash b9aaf0c2586a).I am using a 4090, and when I run the new mistral model, it does not allocate 100% on the GPU:
On the other hand, if I do the same operation with other model, like
gemma3:27b, this error doesn't happen:So my guess is that ollama is estimating wrong the new mistral model size, since it also is smaller than the gemma one:
@metal3d commented on GitHub (Apr 8, 2025):
I've got the same problem on one server with Deepseek-R1, the model is not filled inside the 4 Cards with 8Go VRAM (so 32Go available) while it works on my computer (same distribution, Fedora 41) with one card having 24Go VRAM.
@vini-muchulski commented on GitHub (Apr 8, 2025):
try this! https://www.reddit.com/r/LocalLLaMA/comments/1judvfg/how_to_fix_slow_inference_speed_of_mistralsmall/
@rick-github commented on GitHub (Apr 8, 2025):
The underlying issue is that you don't have enough VRAM to run the model. Setting
num_gpuand PATH tries to skirt the issue, but then you end up with OOMs.They are two different models with different architectures, context window, block count, etc. The memory estimation will not be the same.
When a model is shared across multiple devices, the amount of overhead goes up. That is, a model loaded into a GPU consists of weights, context buffer, computation graph, projector data structures, etc. Some of those allocations need to be replicated across all devices, so multiple copies of those allocations increases the overall VRAM requirement.
@rick-github commented on GitHub (Apr 8, 2025):
See here for ways to deal with OOMs.
@lowlyocean commented on GitHub (Apr 8, 2025):
I meant specifically the issue that causes it to think that 0 layers can be fit on the GPU even though the GPU is bigger than the entire model
@rick-github commented on GitHub (Apr 8, 2025):
The memory estimation logic is receiving attention and there's always room for improvement, but fundamentally you don't have enough VRAM to host the model with the default parameters. There are ways to tweak those parameters as a linked above, but as I mentioned about splitting the model across multiple devices, the extra overhead involved with your setup is just going to make it hard to host mistral-small.
@lowlyocean commented on GitHub (Apr 8, 2025):
Thanks, I want to point out that when I was running the Bartowski Q2_K quant of Mistral Small 3.1 from HF
ollama pull hf.co/bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q2_Kit is able to work with context of 8192, flash attention on, and KV cache q8_0 with
ollama psshowing that it automatically fit entirely into GPU without needing to add libraries to a PATH or setting num_gpu manually.To be clear: there seems to be something specific about having quantized the official Ollama version that's causing this edge case.
@rick-github commented on GitHub (Apr 8, 2025):
The bartowski model doesn't do vision. The projector graph for vision support is large.
@lowlyocean commented on GitHub (Apr 8, 2025):
Thanks, understood. So then, for now if I set num_gpu manually to the max amount (41) I seem to get tokens/s nearly on par with the Bartowski model, and no OOM failures. Future visitors to this issue may want to consider doing that as a workaround
@orrinwitt commented on GitHub (Apr 9, 2025):
I just wanted to chime in and say I've got the same issue. 3 nvidia gpu's available with a combined ~28gb vram (12+8+8), and nvidia-smi shows literally none of the model being loaded to any of them. eventually I get output, since i have enough system ram, but that's obviously unusable output speed.
@metal3d commented on GitHub (Apr 9, 2025):
I had the same... And finally found that reducing context window size to
8096 made everything OK on multiple GPU.
Le mer. 9 avr. 2025, 17:38, Orrin Witt @.***> a écrit :
@orrinwitt commented on GitHub (Apr 18, 2025):
This behavior remains unchanged in 0.6.6-rc2
@thot-experiment commented on GitHub (Apr 25, 2025):
I'm also getting this issue, 32gb GPU, cannot load mistral 3.1 q6k fully into vram, if I let ollama see my second GPU the model gets split taking a total of 24gb. Quanting the same model via ollama down to q5ks allows me to fit it in one gpu, but
ollama psreports 31gb vram used but nvidia-smi and task manager both agree that only 22gb of vram is used (including by other applications). Ram usage was tested during vision inference and the the model outputs confirm that the vision tower is working correctly.This is a breaking bug, it prevents people from loading models that would fit into vram for no reason other than bad usage estimation. I'm using num_gpu 41 btw.
@fbroussais commented on GitHub (May 11, 2025):
I have the same issue with RTX4060ti 16GB : ~3Gb in VRAM and ~11GB in CPU RAM with mistral-small3.1:latest (14GB)
ollama 0.6.8 on Win11
Other models with same size fits more in VRAM (90% for 12GB mistral-nemo:12b-instruct-2407-q8_0)