PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteIf Ollama starts running a model on your CPU after an update, first check where the model was placed, then investigate GPU detection and CUDA initialization. An ollama ps result showing CPU use does not, by itself, prove that the update removed sm_86 kernels. Ollama lists GeForce RTX 30-series GPUs as compute capability 8.6 hardware, but hardware compatibility and the kernels included in a particular package are separate questions.
Check whether Ollama actually loaded the model onto the CPU
Run ollama ps while the model is loaded and inspect the PROCESSOR column. Ollama’s FAQ explains that this column reports model placement:
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- 100% GPU: the model is loaded entirely in GPU memory.
- 100% CPU: it is loaded entirely in system memory.
- A CPU/GPU split: the model is distributed across both. For example, Ollama gives
48%/52% CPU/GPUas a split-placement example.
This confirms where the model is running, not why. A CPU result alone cannot establish that a CUDA kernel is missing. Ollama FAQ.
What the missing sm_86 claim does—and does not—show
Ollama’s GPU documentation lists NVIDIA GPUs with compute capability 5.0 or higher as supported, subject to driver requirements, and identifies GeForce RTX 30-series cards as compute capability 8.6, or sm_86. The documented NVIDIA driver minimum is 550; GPUs with compute capabilities 5.0 through 6.2 require driver 570 or newer. These are compatibility requirements, not proof that every Ollama build or CUDA preset contains kernels for every architecture. Check the current documentation because requirements may change. Ollama GPU support.
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The specific claim that Ollama 0.32.14 omitted sm_86 kernels and thereby broke a CUDA 12 fallback is not independently established by the primary evidence available here. Treat it as an allegation, not a confirmed explanation for a post-update slowdown.
Separate the main GPU failure stages
| What may be happening | What it means | What to check |
|---|---|---|
| GPU not discovered | Ollama has not selected or exposed the GPU to its runtime. | Check startup logs, platform-specific GPU visibility, and whether the installed backend is appropriate. |
| CUDA or backend initialization failed | The GPU may be present, but the runtime could not initialize the selected backend. | Inspect diagnostic logs and NVIDIA-related system errors; verify the driver and installation context. |
| GPU discovered, model partly placed | Some model allocation is on the GPU and some remains in system memory. | Use the CPU/GPU split shown by ollama ps to confirm placement. |
| Model placed entirely on CPU | The model is running in system memory, but this does not identify the cause. | Use logs and environment details to distinguish discovery, initialization, and backend/package issues. |
Collect diagnostics before changing the backend
- Record the setup: note the Ollama version, operating system, GPU model, NVIDIA driver version, and how Ollama is installed or launched.
- Confirm placement: load the affected model and run
ollama ps. Save thePROCESSORresult. - Increase CUDA diagnostics: set
CUDA_ERROR_LEVEL=50in Ollama’s launch environment, reproduce the issue, and review the resulting logs. Ollama’s troubleshooting guide recommends this setting for additional diagnostic output; the exact method for setting an environment variable depends on the operating system and service setup. - On Linux, inspect NVIDIA kernel messages: run
sudo dmesg | grep -i nvrmandsudo dmesg | grep -i nvidiaand look for errors corresponding to the time Ollama starts. - If Ollama runs in a container, verify that the container has GPU access. Host-side GPU support does not by itself guarantee that the container can access it.
These diagnostics help identify the failure stage; they do not prove a missing kernel unless the logs or a reproducible package-specific report establish that cause. See Ollama troubleshooting.
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When a CUDA preset workaround may apply
A July 16, 2026 GitHub issue describes one Windows user’s RTX 3050 Laptop GPU, which is compute capability 8.6. The report says the default GPU discovery path did not find the card and that setting OLLAMA_LLM_LIBRARY=cuda_v13 made it appear in that user’s environment. It also references a separate change adding sm_86 to the cuda_v13_windows preset. The reporter distinguishes that preset change from their remaining default-discovery problem.
This is one user’s report, not a universal fix or confirmation of the alleged Ollama 0.32.14/CUDA 12 regression. Use OLLAMA_LLM_LIBRARY only when the available backend and platform match your installation; forcing a different backend without matching evidence may not solve the problem. See the RTX 3050 Laptop GPU discovery issue.
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Custom builds are a different case
Ollama’s development documentation describes selecting GPU backends and architecture overrides when building from source. Those controls are relevant to custom or locally built binaries, not a general end-user fix for every official installation. If you maintain a custom build, verify the backend and architecture options used for that build rather than assuming an official package’s presets apply. Ollama development documentation.
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