frob
frob
[Server logs](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) will aid in debugging.
Memory estimation for gemma3:27b-it-q4_K_M: ``` time=2025-06-16T16:25:06.614+03:00 level=INFO source=server.go:168 msg=offload library=cuda layers.requested=-1 layers.model=63 layers.offload=63 layers.split="" memory.available="[22.2 GiB]" memory.gpu_overhead="0 B" memory.required.full="20.9 GiB" memory.required.partial="20.9 GiB" memory.required.kv="1.6 GiB" memory.required.allocations="[20.9 GiB]" memory.weights.total="15.4 GiB" memory.weights.repeating="14.3 GiB"...
> What would be the problem? There are layers running in system RAM, where inference is slower. Have you upgraded ollama recently? Downloaded a new version of the model? Upgraded...
> I've upgraded ollama recently yes. Mystery solved. There have been recent changes to the estimation logic to reduce the chance of an OOM. You can force ollama to load...
> And this doesn't seem like an resolution because this guides to load the model on RAM and CPU. It demonstrates how setting `num_gpu` can be used to control the...
[Server logs](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) will aid in debugging.
Nothing in the log indicates a problem. The CUDA backend was loaded, a portion of the layers were loaded into the GPU, the server returned successful HTTP codes. Try setting...
https://github.com/ollama/ollama/blob/main/docs/faq.md#setting-environment-variables-on-windows
Your issue is that responses now take longer after upgrading to 0.9.1? As [explained above](https://github.com/ollama/ollama/issues/11087#issuecomment-2978456204), the new version of ollama is more conservative with memory allocations. You can [override](https://github.com/ollama/ollama/issues/11087#issuecomment-2977115053) `num_gpu`...