Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsIf Ollama is very slow or uses too much memory, first check what it actually loaded: run ollama ps while the affected model is running. Its PROCESSOR and CONTEXT columns show whether the model is using GPU, CPU, or both, and how much context was allocated. Reduce unnecessary context or parallel requests before changing hardware. If the processor split suggests Ollama is not finding a GPU that should be available, investigate GPU detection separately.
Start by checking what Ollama loaded
Load the model that is causing trouble, reproduce the slowdown or memory pressure, then run:
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ollama psRecord the model, the PROCESSOR split, and the CONTEXT value. Also note whether other models are loaded or requests are running at the same time. This is more useful than a general setting that says GPU support is enabled: it describes the workload currently in memory.
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- A fully GPU-resident workload is shown as using the GPU; a CPU/GPU split means some processing is offloaded to the CPU.
- CPU use is not automatically proof of a broken GPU. The model and its working memory may not fit, or Ollama may be unable to discover the device.
- Ollama’s context guide advises avoiding CPU offload for best performance where possible. If the split is unexpected, continue to the GPU-detection checks below.
Ollama defines context length as “the maximum number of tokens that the model has access to in memory.” A larger context can help with long documents or multi-step work, but it also requires more memory. The current upstream documentation publishes these defaults by available VRAM tier:
| Available VRAM | Documented default context |
|---|---|
| Below 24 GiB | 4k |
| 24–48 GiB | 32k |
| 48 GiB or more | 256k |
These are Ollama’s documented defaults, accessed October 4, 2026, not a promise that every model fits at that context or a recommendation for every task. Defaults can change; check the current context-length guide and your installed version.
Ollama uses too much memory: reduce avoidable context and concurrency
Set context to what the task needs
If ollama ps shows a context larger than your task needs, lower it using the Ollama app’s context setting, the OLLAMA_CONTEXT_LENGTH environment variable, or the model’s runtime parameter, as appropriate for your setup. Keep enough context for the work: Ollama recommends at least 64,000 tokens for large-context tasks such as agents, web search, and coding tools. That capacity has a corresponding memory cost.
Reduce simultaneous requests or loaded models
When requests run in parallel, each parallel request increases the model’s allocated context. Ollama’s FAQ describes required RAM as scaling with OLLAMA_NUM_PARALLEL * OLLAMA_CONTEXT_LENGTH. If memory is tight, lower OLLAMA_NUM_PARALLEL or avoid keeping multiple models loaded at once. The tradeoff is less capacity for simultaneous requests and potentially lower throughput for a multi-user or batch workload.
Defaults and controls can differ by deployment and platform. Check the configuration and version actually in use rather than assuming a value from another installation. See Ollama’s FAQ for the documented parallelism and memory controls.
Consider Flash Attention and KV-cache quantization
Ollama says Flash Attention can significantly reduce memory use as context grows; it is used automatically when the selected backend and devices support it. With Flash Attention enabled, the documented KV-cache choices are f16 (the default), q8_0, and q4_0. The FAQ estimates that q8_0 uses about half the KV-cache memory of f16, usually without noticeable quality impact; q4_0 uses about one quarter, with small-to-medium quality loss that can be more noticeable at high context. These ratios apply to the KV cache, not total model memory, and the result depends on the model and task.
KV-cache type is configured globally with OLLAMA_KV_CACHE_TYPE in the documented setup. Check the FAQ for configuration details and compatibility before changing it; do not assume every device or model supports the same options or produces the same quality result.
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Ollama is very slow or the GPU is not being used
If ollama ps shows CPU offload you did not expect, check whether the workload exceeds available memory before treating it as a detection fault. Context, parallel requests, other loaded models, and other GPU workloads all affect whether the model fits. If the model should fit and Ollama still does not use the expected device, use the log path for your platform and follow the matching vendor checks.
| Environment | Where to inspect logs | Relevant checks |
|---|---|---|
| macOS | ~/.ollama/logs/server.log |
Look for GPU discovery or backend errors in the server log. |
| Linux with systemd | journalctl -u ollama |
Inspect the service log; use Ollama’s documented debug logging guidance if more detail is needed. |
| Docker | Container logs | Confirm the container runtime is configured to expose the GPU and check its runtime and driver setup. |
| Windows | %LOCALAPPDATA%Ollamaserver.log |
Inspect the server log for device discovery and backend errors. |
For NVIDIA systems, check driver compatibility and the NVIDIA UVM module where relevant; for GPU use in containers, verify the NVIDIA container runtime setup. For AMD on Linux, check driver compatibility and whether the process has the required permissions for /dev/kfd. Do not run privileged driver or device-permission commands unless the platform and log symptoms match the issue. Ollama’s troubleshooting guide documents platform-specific logs and checks.
GPU not detected is a different problem from a model that does not fit in GPU memory. Ollama’s GPU support page lists supported hardware and requirements, including NVIDIA compute capability and driver requirements, Apple GPU support through Metal, and Vulkan support paths. Verify the current guidance for your operating system and GPU generation because support details can change.
Only consider hardware after measuring the workload
If the GPU is detected but the model still exceeds available memory after you have reduced unnecessary context and concurrency, compare hardware against the workload you actually run. Check available VRAM, Ollama support, model and quantization fit at your required context, competing GPU use, power delivery, case clearance, platform compatibility, and cost. Ollama’s supported list includes the GeForce RTX 5060, but inclusion on that list does not establish that it can fit a particular model or deliver a particular speed.
Ollama announced a new model scheduling system on September 23, 2025, saying that it measures exact memory requirements rather than relying on earlier estimates and describing fewer out-of-memory crashes, increased GPU allocation and utilization, and improved multi-GPU scheduling. Those stated benefits apply to models implemented in that engine; they do not establish that every model uses it. Neither the support list nor the scheduling announcement provides a universal VRAM requirement or tokens-per-second forecast. Actual performance depends on model, quantization, context, concurrency, GPU, driver and backend, and installed Ollama version.
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