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Before changing hardware, check where Ollama is placing the model and how much context and concurrency the workload requests. Run ollama ps while the model is loaded: its PROCESSOR field shows whether processing is on the GPU, CPU, or split between them, and the output also reports model size and context. A CPU share can help explain slow inference, but it is a clue—not proof that memory is the only cause.
1. Capture the workload and check actual allocation
Record the Ollama version, model tag, operating system, GPU/backend, context setting, and whether other requests or models are active. Then load the model and run:
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ollama psRead the PROCESSOR, SIZE, and CONTEXT values. Ollama documents this output as a way to tell whether a model is on GPU, CPU, or split across both. A split or CPU allocation may account for slower generation; allocation alone does not identify the cause of every slowdown. See Ollama’s FAQ and context-length guide.
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2. Reduce memory demand before changing hardware
Choose a context that fits the task
Context is the maximum number of tokens available to the model in memory. Ollama warns that increasing context requires more memory. Its current documentation, accessed October 4, 2026, lists these default context lengths by available VRAM:
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| Available VRAM | Ollama documented default context |
|---|---|
| Below 24 GiB | 4k |
| 24–48 GiB | 32k |
| 48 GiB or more | 256k |
These are documented defaults, not guarantees that any particular model will fit. Use a smaller context when memory is tight, provided it still covers the task. Ollama recommends at least 64,000 tokens for tasks such as web search, agents, and coding tools, but that is not a universal troubleshooting setting: raise context only if the machine has enough memory for the model and workload.
Set context in the place you run Ollama
- Ollama app: adjust the context slider.
- Server: set
OLLAMA_CONTEXT_LENGTHin the server environment. - Interactive session: in
ollama run, enter/set parameter num_ctxfollowed by the desired value. - API: provide
num_ctxin the request’soptions.
Use the matching interface for the process that actually serves the model; changing a client setting will not necessarily change a separately configured server. The context guide and FAQ document these controls.
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Reduce concurrency and unload models you do not need
Parallel requests can multiply context allocation: Ollama says memory use for parallel processing increases with the number of parallel requests. Under server load, lower OLLAMA_NUM_PARALLEL or reduce the number of concurrently loaded models with OLLAMA_MAX_LOADED_MODELS. Ollama’s FAQ says simultaneous loading and inference depend on available system memory for CPU inference or VRAM for GPU inference.
When a model is idle, unload it with ollama stop <model>; API callers can set keep_alive to zero. OLLAMA_MAX_QUEUE controls how many requests wait when the server is busy; it does not add memory for inference or make a model fit. These behaviors and settings are covered in the Ollama FAQ.
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Optional advanced cache settings
If your installed version and model support them, Ollama documents automatic Flash Attention when the backend and device support it, a force-on option of OLLAMA_FLASH_ATTENTION=1, and OLLAMA_KV_CACHE_TYPE for the K/V cache when Flash Attention is enabled. The cache type is a global option; the FAQ identifies f16 as its default. These settings are not a substitute for reducing an unnecessarily large context or concurrency, and support depends on the backend and device. Check the current FAQ before changing them.
3. If GPU allocation looks wrong, diagnose detection separately
First distinguish a GPU that Ollama cannot discover from one it detects but cannot use for the full workload. If logs indicate initialization or visibility trouble, inspect them using the relevant platform path in Ollama’s troubleshooting guide:
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- macOS:
~/.ollama/logs/server.log - Linux with systemd:
journalctl -u ollama --no-pager --follow --pager-end - Docker:
docker logsfor the Ollama container - Windows: logs under
%LOCALAPPDATA%Ollama; quit the app and launch it withOLLAMA_DEBUG=1for more detail.
Linux NVIDIA containers
Test whether Docker can access the GPU with:
docker run --gpus all ubuntu nvidia-smiIf the command fails, the container cannot see the GPU through that setup. Ollama’s troubleshooting guide also recommends checking or reloading the UVM driver, rebooting, and using current NVIDIA drivers.
Linux AMD
Check that the user has the required video and render group access and that a container can access /dev/kfd and /dev/dri. For additional diagnostics, Ollama documents OLLAMA_DEBUG=1 and AMD_LOG_LEVEL=3. Its current troubleshooting page warns that AMD discovery timeouts can occur when an older ROCm kernel driver is incompatible with the ROCm 7 libraries bundled by Ollama, and recommends upgrading the driver with AMD’s installation utility. Driver guidance can change; verify it against the current Ollama troubleshooting page and AMD’s instructions for your release.
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4. Choose a model or hardware only after the checks
If Ollama detects the GPU but ollama ps shows CPU or split allocation, first try a smaller context and reduce concurrent requests or loaded models. Then test a model/configuration that better fits the machine. If GPU memory remains the demonstrated bottleneck and those changes do not meet the task, a GPU with more usable VRAM may help—but there is no source-backed universal VRAM target or single best card for every Ollama model.
Check the selected model’s memory needs and Ollama’s current GPU support documentation before buying or changing a GPU. Compatibility, drivers, model, context, and total concurrent workload all affect whether an upgrade addresses the problem.
Why an OOM may vary by model or Ollama version
Model scheduling has changed. In an announcement dated September 23, 2025, Ollama said its newer scheduler measures exact memory needs rather than relying on prior estimates and reported fewer out-of-memory crashes as a benefit. The announcement says the scheduler was enabled for models implemented in Ollama’s new engine, with more models moving over; it does not establish that every model or version uses it. See Ollama’s scheduling announcement.
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