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hoyyeva/anthropic-local-image-path
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dhiltgen/llama-runner
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Originally created by @micseydel on GitHub (Apr 2, 2026).
Original GitHub issue: https://github.com/ollama/ollama/issues/15228
What is the issue?
I have a 64gb M4 Mac I've been running llama3.3:70b on for some time. It last worked on 2026-03-07 and failed on 2026-03-11. I suspect I updated Ollama but don't track it and I'm not sure where to look in the logs. Here's an example failure:
and I've included the contents of ~/.ollama/logs/server.log after hitting enter above in Relevant log output.
Relevant log output
OS
macOS
GPU
Apple
CPU
Apple
Ollama version
0.19.0
@rick-github commented on GitHub (Apr 2, 2026):
Include the log from before the crash.
@micseydel commented on GitHub (Apr 2, 2026):
Here's the full thing all at once:
@micseydel commented on GitHub (Apr 5, 2026):
I didn't try raw llama.cpp before, but got it working on two machines. The 70b models I tried crawled, to the point of being unusable. Prompts used to take 2-3 minutes through Ollama's web API, but I can't say if direct llama.cpp outside Ollama used to work for my configuration or if I'm actually doing things right.
Apologies if the double-comment+delete caused confusion earlier, I'd be happy to provide llama.cpp logs if you let me know what you do (and maybe don't) want. I appreciate your project, it worked for me for months and was much easier to get going with than raw llama.cpp (which also hasn't saved me 😅).
@rick-github commented on GitHub (Apr 5, 2026):
OLLAMA_CONTEXT_LENGTHis not set so the server is using a default of 256k, based on the availabke VRAM.256k is larger than the model was trained with, so the context size is set to the maximum supported by the model, 128k.
Because of the large context, only 30 layers fit in the GPU. The model is being loaded with the llama.cpp engine which sometimes gets the memory estimation wrong, so what I think is happening here is that the runner is trying to allocate more memory than is available on the GPU. Ways to deal with OOMs are shown here, but the easiest thing to do would be to set
OLLAMA_CONTEXT_LENGTHto something more reasonable. If you do need a larger context, setOLLAMA_NEW_ENGINE=1to get more accurate memory estimation.@PureBlissAK commented on GitHub (Apr 18, 2026):
🤖 Automated Triage & Analysis Report
Issue: #15228
Analyzed: 2026-04-18T18:22:53.126160
Analysis
Implementation Plan
This issue has been triaged and marked for implementation.
@micseydel commented on GitHub (Apr 18, 2026):
Changing the context size in the Ollama GUI got things working again. (I also tinkered more with raw llama.cpp and couldn't get it working.)
If you think this ticket could be resolved by someone new to the codebase, I might be down to volunteer. I'd prefer newcomers have a "just works" experience like I did when I first tried Ollama.