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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteStart by reducing the model’s context length, then lower concurrency or batch capacity, switch to a smaller or more heavily quantized model, and clear other GPU workloads. If the model still cannot fit after those changes, the remaining requirement may exceed your available GPU memory; at that point you need a different model configuration or hardware that can distribute the workload across more GPUs.
Do not apply flags from one runtime to another. The right fix depends on whether you are using vLLM, Ollama, llama.cpp, or another launcher; which model and quantization you loaded; how much VRAM is free; and whether the failure occurs while loading the model, processing a prompt, or generating output.
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Why a local language model runs out of memory
A model’s weight file is only one part of its memory footprint. The runtime also allocates memory for the context (the tokens the model can currently access), attention and key-value caches, temporary tensors, batching, and serving features. vLLM’s documentation specifically identifies CUDA graphs as an additional GPU-memory consumer. NVIDIA’s NIM guidance likewise warns that CUDA graph capture can require extra memory, with the amount depending on the model and GPU.
That is why a model that appears to fit when you look only at its file size can still fail during startup, prompt processing, or generation. A long context window, several simultaneous sequences, or an unrelated process already using VRAM can push the allocation over the limit.
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Context is especially easy to overlook. Ollama defines context length as the maximum number of tokens the model can access in memory, and its documentation provides a setting to change it. Some runtimes may otherwise try to reserve a very large, model-supported context. NVIDIA’s llama.cpp guidance warns that the default context allocation can attempt to use the model’s maximum supported context, even when your task needs far less.
First, identify the failure precisely
Before changing settings, record the configuration that produced the error. These details determine which remedy is applicable and prevent mixing controls from unrelated runtimes.
- Runtime: vLLM, Ollama, llama.cpp, or another engine.
- Model and quantization: include the exact model variant and precision or quantization format.
- GPU and free VRAM: note total memory and how much was available at the time of the failure.
- Configured context: record the context-window setting, if one is exposed.
- Workload: single prompt, interactive chat, batch job, or multi-user service.
- Failure stage: model load, prompt processing, or token generation.
A failure at model load usually means the weights, runtime allocations, or initial context reservation cannot fit. A failure only when a long prompt is submitted points more strongly to context or cache growth. A failure when several requests arrive together points to concurrency or batch capacity. Treat those as clues, not absolute rules: runtimes allocate memory differently.
Fixes in the order that usually preserves the most capability
1. Reduce the context length to what the task needs
Lower the context-window setting and retry before changing the model. Context length is a ceiling, not a requirement that every prompt must use. If your task involves short questions, code snippets, or small documents, a very large window reserves memory you may never use.
Use the runtime’s own control:
- vLLM: lower
max_model_lenin the server configuration or launch arguments. - Ollama: reduce the context-length setting exposed by the application or its configuration.
- llama.cpp: lower the
--ctx-sizevalue.
NVIDIA’s llama.cpp example uses 4096 as a possible context size when CUDA memory is insufficient at startup. That is an example setting, not a universal target. Choose a value that covers the largest prompt and response you actually need, with some headroom for the runtime.
The trade-off is straightforward: a smaller context can prevent long-document prompts, extended conversation history, or large retrieval batches from fitting. If reducing it solves the error, increase it gradually until you find the highest reliable value rather than returning immediately to the model’s maximum.
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2. Reduce concurrency and batch capacity
If you are serving requests rather than running one interactive session, limit the number of sequences the runtime keeps active. In vLLM, reduce max_num_seqs as well as max_model_len. Fewer simultaneous sequences generally means fewer context and cache allocations.
This control is specific to serving workloads. Do not present vLLM’s max_num_seqs as an Ollama or llama.cpp setting. In a single-user desktop session, concurrency may not be the cause; reducing it will not help if the model cannot load even with one sequence.
Lower concurrency trades throughput for a smaller memory footprint. A server may process the same total number of requests more slowly, but it can remain stable instead of failing when several prompts arrive together. Make the change while monitoring the request pattern that caused the error, because an apparently safe limit can still be exceeded by unusually long contexts.
3. Choose a smaller model or lower-memory quantization
If context and concurrency reductions are not enough, select a model variant with fewer parameters or a more memory-efficient quantization. NVIDIA’s llama.cpp troubleshooting recommends choosing a smaller quantization when CUDA memory is insufficient. vLLM and NVIDIA guidance also describe quantization as a way to reduce memory use.
Quantization is a trade-off, not a free reduction. The vLLM project states: “Quantized models take less memory at the cost of lower precision.” The effect on output quality, instruction following, and speed depends on the model and the quantization method; the available documentation does not establish one universal quality loss or performance gain.
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Changing quantization can also alter where memory is used. Confirm that the runtime actually loaded the intended variant rather than silently falling back to another format. Record the model name and quantization alongside your context and concurrency settings so you can reproduce a working configuration.
4. Clear occupied GPU memory and runtime overhead
Check current GPU use before concluding that the model itself is too large. Another model, a graphics application, a notebook, or a previous server process may already be holding VRAM. Close unrelated workloads and stop idle model processes, then retry with the same configuration.
For Ollama, the documented ollama stop command immediately unloads a model from memory. Use it when a resident Ollama model is occupying capacity you need for another model. If more than one service is using the GPU, stop or reconfigure those services as well.
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Runtime features can consume memory even when no other application is visible. vLLM and NVIDIA’s NIM documentation call out CUDA graphs as an additional allocation. The amount is model- and GPU-dependent, so there is no reliable universal overhead percentage to subtract. Leave headroom rather than sizing a configuration to the last megabyte of nominal VRAM.
On macOS, Ollama’s FAQ documents a GPU-passthrough limitation when Ollama runs inside Docker Desktop. Treat that as a platform-specific issue: if your installation is macOS plus Docker Desktop, verify whether the container can access the GPU before changing model settings. It is not a universal limitation of every Ollama installation.
5. Re-test at the stage that failed
After each change, repeat the operation that originally failed. A model-load test does not prove that a long prompt or concurrent serving workload will fit. Use a short prompt first, then exercise the maximum context and request count you intend to support.
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- If loading now succeeds but a long prompt fails, reduce context further or shorten the prompt and conversation history.
- If one request works but multiple requests fail, lower vLLM’s
max_num_seqsor otherwise reduce batch pressure in the runtime that supports it. - If generation fails after a long response begins, leave more headroom by reducing context, concurrency, or model memory use.
- If every attempt fails at startup, move to a smaller model or quantization and verify that other GPU users are stopped.
Runtime-specific procedures
vLLM
vLLM is commonly used to serve models and can hold multiple sequences at once. Its documented memory levers are:
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max_model_lento the largest context your application genuinely needs. - Lower
max_num_seqswhen several requests or batched sequences are active. - Select a quantized model if the weights and runtime allocations still exceed available VRAM.
- Account for CUDA graph memory and other runtime allocations; leave headroom instead of assuming all nominal VRAM is available to model data.
Change one or two related settings, restart the server, and test the same request pattern. A setting that fixes an idle startup can still be too aggressive for production traffic if requests have longer prompts than your test.
Ollama
For Ollama, start with the context-length control in its settings or configuration. Reduce the value, restart or reload the model as required by your installation, and test with a prompt representative of your normal use.
If another model is resident, run ollama stop to unload it immediately. This is useful when switching between models or when an earlier session left a model in memory. Then retry before changing quantization.
Keep platform details in the diagnosis. Ollama’s documentation notes a macOS Docker Desktop GPU-passthrough limitation; a container that cannot access the GPU may behave differently from a native installation. Confirm the deployment path before interpreting a memory error as evidence that the model is too large.
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llama.cpp
When llama.cpp reports a CUDA out-of-memory error at startup, NVIDIA’s playbook recommends lowering --ctx-size or selecting a smaller quantization. Start with the context reduction because it preserves the same model and precision.
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The frequently cited value 4096 is only an example from the troubleshooting guidance. Do not treat it as a universal recommendation: the appropriate context depends on your prompt sizes, model, quantization, GPU, and other allocations.
If a lower context still cannot load the model, select a smaller quantization or a smaller model. Re-test with the same prompt and record the working --ctx-size and model format so a later upgrade does not silently restore an oversized default.
How to choose between remedies
When several changes could solve the error, compare them by the capability you give up and the operational change required.
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|---|---|---|---|
| Reduce context length | Lowers memory reserved for accessible tokens | Long prompts, document history, and long conversations may no longer fit | The model fails with a large context or at startup |
| Reduce concurrent sequences | Limits simultaneous request and cache allocations | Lower throughput or more waiting under load | vLLM serving fails when multiple requests arrive |
| Use quantization | Reduces model-memory use through lower precision | Precision and output behavior can change | The model and required context still exceed VRAM |
| Use a smaller model | Reduces weight and runtime requirements | Potentially less capability for difficult tasks | The current model cannot fit even with conservative settings |
| Stop other workloads | Reclaims currently occupied VRAM | Those applications or models cannot run simultaneously | GPU monitoring shows unrelated memory use |
| Add or distribute across GPUs | Provides more aggregate GPU capacity | Requires compatible hardware and a more complex deployment | Configuration changes cannot make the required model fit |
The sources do not provide a general number for memory saved by any remedy. Measure your own runtime and workload rather than promising a fixed reduction.
When more hardware is the appropriate fix
Hardware should be the last step after reducing context, concurrency, model size, and avoidable memory use. If the model configuration you require still exceeds available GPU memory, NVIDIA’s NIM troubleshooting describes profiles that distribute a model across more GPUs.
This does not establish a universal graphics-card requirement or a single sufficient VRAM capacity. Match any hardware decision to the exact model, quantization, context length, runtime, and number of concurrent requests. A card with more VRAM can help, but it may not solve a deployment whose runtime overhead or context target remains too large.
Before buying hardware, write down the configuration that failed and the smallest configuration that worked. That comparison tells you whether you need more capacity for weights, more headroom for context, or more parallel capacity for serving. It also prevents paying for hardware when a context or concurrency setting would have solved the problem.
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A repeatable diagnostic checklist
- Identify the runtime and do not mix its flags with another engine’s controls.
- Record model, quantization, GPU, free VRAM, context setting, workload, and failure stage.
- Stop unrelated GPU applications and, for Ollama, unload idle models with
ollama stop. - Reduce context:
max_model_lenin vLLM, the context setting in Ollama, or--ctx-sizein llama.cpp. - For vLLM serving, reduce
max_num_seqsif concurrent requests are involved. - Retry the same operation, then test a realistic long prompt and request load.
- If it still fails, choose a lower-memory quantization or smaller model and verify the loaded format.
- Account for CUDA graph and other runtime allocations; leave practical headroom.
- Only then evaluate distributing the model across more GPUs or upgrading hardware.
Common mistakes that keep the error alive
- Changing only the model file: a large context or several active sequences can still exhaust memory with smaller weights.
- Assuming nominal VRAM is free: the operating system, display server, another model, and runtime features may already consume part of it.
- Using a flag from the wrong runtime:
max_num_seqsis a vLLM control, not a general Ollama or llama.cpp option. - Treating 4096 as a universal llama.cpp value: it is an example setting, not a guaranteed fit.
- Testing only a short prompt: startup success does not prove that your intended context or response length will fit.
- Buying hardware before measuring the configuration: without the model, context, quantization, and concurrency requirements, a graphics-card recommendation is guesswork.
What a successful fix looks like
A reliable configuration is not merely one that starts once. It loads the intended model and quantization, handles the largest context you actually plan to send, survives the expected number of simultaneous sequences, and leaves enough memory for runtime overhead. Record those settings and the test workload. If a future model update or deployment change raises the default context or enables another memory-consuming feature, you can compare it with the known-good baseline instead of troubleshooting from scratch.
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