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[GH-ISSUE #11041] llama3.2-vision enters infinite loop on Kubernetes with NVIDIA A10 GPU #7283
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opened 2026-04-12 19:20:00 -05:00 by GiteaMirror
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Reference: github-starred/ollama#7283
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Originally created by @LaeraFelipe on GitHub (Jun 10, 2025).
Original GitHub issue: https://github.com/ollama/ollama/issues/11041
What is the issue?
I'm running Ollama with the llama3.2-vision:latest model on a Kubernetes cluster using an NVIDIA A10 GPU. Alongside this model, I'm also running others such as gemma3:4b, llama3.2:3b, etc.—all of which work normally. However, when I run prompts with llama3.2-vision, it initially appears to work fine but eventually enters an infinite loop, as shown in the image below:
In the following image, you can see that Ollama is the only process using the GPU, and during the loop, it fully utilizes the GPU's processing power. Note that this container has no CPU, memory, or GPU limits defined.
The processing only stops when the prompt is manually canceled.
After this initial loop scenario, every subsequent attempt to send an image and ask a question results in the same behavior:
After approximately 5 minutes, the model is unloaded from memory. If I then submit a new prompt, the loop scenario occurs again.
Here are the details of the GPU driver I'm using:
I also have another server with the same configuration, running Ollama version 0.6.7 via Docker Compose, using a llama3.2-vision:latest model downloaded around six months ago—and that setup works perfectly.
Here is the nvidia-smi output from that machine:
Relevant log output
OS
Docker
GPU
Nvidia
CPU
Intel
Ollama version
0.9.0
@rick-github commented on GitHub (Jun 10, 2025):
Do you have logs from around the time the request was manually cancelled?
@LaeraFelipe commented on GitHub (Jun 10, 2025):
Yes! These are the latest logs:
@rick-github commented on GitHub (Jun 10, 2025):
Your context window is too small. The runner fills up the available context space and then has to shift the context buffer to make room for new tokens. This results in the head of the buffer being lost which can cause the model to lose coherence, after which it can get in a state of emitting random tokens until interrupted or the internal repeated token limit triggers and stops the generation.
In the ideal world a buffer shift shouldn't cause this loss of coherence, so this bears looking into. A similar problem was recently discussed in the discord. In the meantime, increasing the size of the context buffer or setting
num_predictto limit the number of tokens generated will mitigate the issue.@LaeraFelipe commented on GitHub (Jun 11, 2025):
Thanks @rick-github!
Is there a way to set the num_predict variable through an environment variable, besides setting it via the API?
From what I understand, the default value is -1, which means infinite — is that correct?
And for the context size, the corresponding environment variable would be OLLAMA_CONTEXT_LENGTH, right?
@rick-github commented on GitHub (Jun 11, 2025):
num_predictcan be set in the API or in a Modelfile. Unfortunately there's no global environment variable that can be set. The default value of -1 is infinite. There is an open PR to allow setting it to -2 to limit it to the number of free token positions in the buffer.Context size is settable via
OLLAMA_CONTEXT_LENGTHor again via API or Modelfile. Since it's global, it will affect all loaded models.@LaeraFelipe commented on GitHub (Jun 11, 2025):
@rick-github ,I believe the last log you saw was already from the other chat I opened to show what it looked like afterward. I just ran a new simulation on the server, but I couldn’t find the log you mentioned.
I also managed to reproduce the scenario on my laptop using Docker Compose with an RTX 4060, and I didn’t see that log there either.
Here are the logs:
The request I had to stop because it entered a loop is the last one, which ran for 5 minutes.
I also tried setting num_predict to -2 in the API requests, but it didn’t work.
@rick-github commented on GitHub (Jun 11, 2025):
Set
OLLAMA_DEBUG=1.@LaeraFelipe commented on GitHub (Jun 11, 2025):
@rick-github, here are the logs with OLLAMA_DEBUG=1 enabled, running via Docker Compose on an A10 GPU.
The logs above are from version 0.9.0 running the latest model.
Note: I cloned the model data from another server that was running Ollama version 0.6.7, along with a copy of the model downloaded six months ago, and deployed it to Kubernetes — it's now working correctly. It seems the issue is related to the latest versions of Ollama and the llama3.2-vision:latest model.
@rick-github commented on GitHub (Jun 12, 2025):
This logs shows two apparently successful generations. It's more helpful if you post the full log rather than a handful of lines.