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Local-model out-of-memory (OOM) errors are usually configuration problems before they are hardware problems. Reduce the context window to what the task needs, limit concurrent sequences or batch capacity, unload other models and GPU workloads, and then try a smaller or lower-precision model. Only after those changes should you decide that the model requires more GPU memory or multiple GPUs.
The correct fix depends on the runtime, the model and quantization, the configured context length, available VRAM, and the point at which the failure occurs. Use the sequence below, and apply only the flags belonging to your runtime.
Start by identifying what is failing
An OOM during model loading has a different cause from an OOM after a long prompt or during generation. Before changing settings, record the following:
- Runtime: vLLM, Ollama, llama.cpp, or another engine.
- Model and quantization: include the exact model variant and precision or quantization.
- GPU capacity: total and currently available VRAM. On NVIDIA systems,
nvidia-smishows active processes and memory use. - Context setting: the maximum token window configured for the model.
- Workload: single-user generation, a server handling concurrent requests, or a batched job.
- Failure stage: startup/model load, prompt processing, or token generation.
Do not transfer a command-line option from one runtime to another. A setting such as vLLM’s --max_num_seqs is not an Ollama or llama.cpp setting, and changing an unrelated flag will not reduce memory use.
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Why a model can exceed VRAM even when its weight file fits
The downloaded model weights are only one part of the allocation. The runtime also needs memory for the token context, attention key-value (KV) cache, temporary workspaces, and—in some serving engines—execution features such as CUDA graphs. A model can therefore load successfully with a short prompt and fail when the context grows or when a second request arrives.
Context length creates a variable allocation
Context length is the maximum number of tokens the model can keep available for a request. Increasing it reserves room for longer prompts and conversation history, but also increases memory demand. A setting at the model’s advertised maximum is often unnecessary for a short coding question or chat session.
Concurrency multiplies working memory
Serving several sequences at once requires capacity for each sequence’s state. A configuration that works for one request may fail when the server admits more sequences or uses a larger batch.
Runtime overhead is model- and GPU-dependent
vLLM documentation identifies CUDA graphs as an additional GPU-memory consumer. NVIDIA’s NIM troubleshooting guidance also notes that CUDA graph capture can require extra memory. Neither source gives a universal overhead number: the amount depends on the model, runtime configuration, and GPU.
Use this order of operations
- Lower the context window. Set it to the longest prompt and answer your application actually needs, with a modest margin. Retry before making any hardware decision.
- Reduce concurrency or batch capacity. If you are serving multiple requests, admit fewer sequences at a time. This is especially important for vLLM.
- Unload other memory consumers. Stop resident models and close GPU-heavy applications. Confirm that free VRAM increased before retrying.
- Choose a smaller or quantized model. Lower-precision quantization generally reduces model memory, but it changes numerical precision and can affect output quality or speed.
- Account for runtime overhead. Leave headroom for CUDA graphs and temporary allocations rather than treating all VRAM as available for weights.
- Consider additional GPUs only if the required configuration still does not fit. Multi-GPU profiles can distribute a model, but the appropriate hardware depends on the model, context, runtime, and workload.
Reduce the context length first
Context is the most broadly applicable software lever because all three runtime paths covered here expose one.
vLLM
Set --max-model-len to the largest token window your application needs. For example, a document-summarization service that never receives more than a few thousand tokens should not reserve the model’s maximum context merely because the model supports it. Test with a representative prompt and increase the value only when a real workload requires it.
Ollama
Change the context-length setting in your Ollama configuration. The label and configuration location can vary by release and installation method, so verify the value shown by your current client or server. A shorter context means less conversation history can remain in memory, but it can be preferable to an engine that cannot load or generate at all.
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llama.cpp
Pass a lower value to --ctx-size. NVIDIA’s llama.cpp troubleshooting example uses 4096 when CUDA memory is insufficient at startup. That is an example setting, not a universal target; choose a value based on your prompt and response requirements.
./llama-cli -m model.gguf --ctx-size 4096The executable name and model path in this example depend on your build. The memory-saving action is the --ctx-size reduction, not the example’s particular number.
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Estimate the context you actually need
Measure the largest real request rather than guessing from the model card. Include system instructions, retrieved passages, chat history, the expected answer, and a safety margin. If a workflow summarizes documents, chunk the documents and process them separately instead of reserving the model’s full advertised window. If a chatbot keeps an unlimited transcript, add a summarization or truncation policy so history does not grow until decoding fails.
Changing the maximum context does not rewrite a prompt that is already too long. The client may still receive a context-limit error, so make the application enforce the same limit and report a clear message when it truncates or rejects input.
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For vLLM servers, reduce both the context limit and the number of sequences that can be processed concurrently. Set --max_num_seqs to a lower value when a server is admitting more requests than the GPU can hold.
vllm serve /path/to/model --max-model-len 4096 --max_num_seqs 1The values above illustrate the controls; they are not universal sizing advice. A single-user service may tolerate one sequence, while a production endpoint may need more. Increase concurrency only after a representative load test remains within the available memory.
Reducing --max_num_seqs protects capacity for each active request and can lower throughput. Reducing --max-model-len limits the amount of prompt and history that each request can retain. Treat those as deliberate service-level trade-offs, not as quality improvements.
Separate queueing from memory capacity
If a service cannot safely run many sequences, queue excess requests rather than allowing the runtime to allocate them all at once. A queue increases latency, but it avoids an abrupt OOM and gives you predictable concurrency. Test both one-request and peak-request cases; a startup test alone cannot reveal a batch-allocation problem.
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Quantization
Quantization stores weights with fewer bits. vLLM’s documentation summarizes the trade-off directly: “Quantized models take less memory at the cost of lower precision.” Lower precision can change output quality, numerical behavior, and performance, and the effect depends on the model and quantization method. There is no single quantization level that is best for every task.
When switching variants, compare the exact model family, quantization format, context requirement, and expected workload. A smaller model in a higher-precision format and a larger model in a lower-precision format can have different memory and quality characteristics; the weight-file label alone is not enough to predict the complete runtime allocation.
Smaller model
If context and concurrency reductions are not enough, select a model with fewer parameters or a lower-memory variant. This reduces the baseline weight allocation, leaving more room for the KV cache and runtime workspaces. The trade-off is usually less task capacity or accuracy, so validate it against the prompts your application actually serves.
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Validate the replacement
Run a small acceptance set after changing models or precision: factual answers, structured output, tool calls, long-context cases, and the languages your users need. Check latency and token throughput as well as answer quality. A model that fits but fails your format or accuracy requirements is not a complete fix.
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Inspect active processes
Check current GPU usage before concluding that the model cannot fit. A previously loaded model, a graphics application, a notebook kernel, or another service may be consuming VRAM. On NVIDIA hardware, run nvidia-smi and inspect the process list and free-memory figures. Close or stop unrelated workloads, then retry the same model configuration.
Unload an Ollama model
Ollama’s FAQ documents ollama stop as a way to immediately unload a model from memory:
ollama stop <model-name>Replace <model-name> with the model identifier shown by your installation. This is useful when another resident Ollama model is occupying VRAM; it does not make an intrinsically oversized model fit.
Allow for CUDA graph capture
Some serving configurations capture CUDA graphs to improve execution efficiency. Capture can require additional memory, and the amount is not a fixed percentage. If the model appears to fit by its weight size but startup still fails, treat graph and workspace allocations as part of the budget. Use the runtime’s documented memory-management options rather than assuming every byte reported as free can be allocated to weights.
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Check the process after stopping it
Some applications release memory asynchronously. After stopping a model, wait for the process to exit and refresh the GPU monitor before retrying. If memory remains occupied, identify the owning process rather than repeatedly launching new copies of the model. In managed servers, confirm that an old worker or failed container has not left a resident process behind.
Runtime-specific troubleshooting
| Runtime | Controls documented for reducing memory | Important limitation |
|---|---|---|
| vLLM | Lower --max-model-len; lower --max_num_seqs; choose quantization; account for CUDA graph memory. |
Concurrency and batching controls apply to vLLM serving workloads, not automatically to desktop runtimes. |
| Ollama | Change the context-length setting; use ollama stop to unload a resident model. |
GPU behavior depends on platform and installation. macOS Docker Desktop has a documented GPU-passthrough limitation. |
| llama.cpp | Lower --ctx-size; select a smaller quantization when CUDA memory is insufficient at startup. |
The 4096 value shown in NVIDIA’s example is illustrative, not a universal recommendation. |
Match the symptom to the likely remedy
Failure immediately during startup
Startup failure usually means the baseline model, initial context allocation, or runtime setup exceeds available memory. Lower the context setting first, then try a smaller quantization or model. Check for other GPU processes and leave headroom for runtime allocations.
Failure only with long prompts
A long prompt increases the active context and KV-cache requirement. Reduce the context window, shorten retrieved documents or conversation history, or process the material in smaller chunks. Do not raise the maximum context simply because the model advertises one.
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Failure only when a second request arrives
This pattern points to concurrency or batch capacity. In vLLM, lower --max_num_seqs and test again. If the service must handle the same concurrency, a lower-memory model configuration or additional GPU capacity may be necessary.
Failure during generation after prompt processing succeeds
Generation can require additional temporary workspace and cache growth. Reduce context and concurrency, stop other GPU workloads, and test with a smaller or quantized model. A successful prompt-processing phase does not prove that the complete generation workload fits.
Failure after a software or model update
Record the previous working runtime, model revision, context, and concurrency values. Updates can alter defaults or memory behavior. Reapply explicit limits instead of relying on a new default, then compare the same prompt and workload. Roll back only if your normal configuration cannot be restored, and verify compatibility with the updated runtime.
Compare fixes by what they sacrifice
| Remedy | Memory effect | What you give up | Configuration or hardware? |
|---|---|---|---|
| Shorter context | Reduces context and cache allocation; the sources do not provide a universal number of bytes saved. | Less prompt history and a shorter maximum input. | Configuration change. |
| Fewer concurrent sequences | Reduces simultaneous per-request state. | Throughput and concurrent capacity. | Configuration change; primarily relevant to serving. |
| Quantization | Reduces weight memory. | Lower numerical precision, with quality and performance effects dependent on the model and quantization. | Model/configuration change. |
| Smaller model | Reduces baseline weight allocation. | Potentially less capability or accuracy for demanding tasks. | Model change. |
| Unload other workloads | Returns occupied VRAM to the available pool. | Those other applications or resident models cannot run simultaneously. | Operational change. |
| More GPUs | Allows a supported profile to distribute the model across devices. | Additional hardware, power, setup complexity, and inter-GPU communication overhead. | Hardware and configuration. |
Because the cited documentation does not establish a cross-runtime memory formula, treat “memory saved” as workload-dependent rather than as a guaranteed percentage.
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Consider hardware only after testing a shorter context, lower concurrency, unloaded memory, and a smaller or quantized model. If the application genuinely requires the larger model, longer context, and current concurrency together, NVIDIA’s NIM troubleshooting guidance describes profiles that distribute a model across more GPUs.
That guidance does not establish a universal graphics-card requirement or a minimum VRAM number. Match any proposed GPU or workstation to the exact model, quantization, context, runtime, and number of simultaneous requests. A card that fits the weights may still fail once cache, CUDA graphs, and other allocations are included.
If you shop for hardware, treat higher-VRAM graphics cards as a conditional option, not an automatic cure. Check power, cooling, host memory, driver support, physical clearance, and whether your runtime supports splitting the model across the selected devices. Extra GPUs also add setup complexity and may affect throughput because data must move between devices.
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- Stop resident models and close unrelated GPU workloads.
- Record free VRAM and the runtime’s current context and concurrency settings.
- Start with a short, known prompt and one request.
- Set the context to a conservative value appropriate to that prompt.
- Run a generation test and observe memory during both prompt processing and decoding.
- Increase context gradually only when the application needs it.
- For a server, increase concurrent sequences gradually and test representative simultaneous requests.
- If the smallest acceptable configuration still fails, switch to a lower-memory model or quantization.
- Document the working configuration so an update or deployment change does not silently restore an oversized context or concurrency limit.
What to log during each test
Capture the model identifier, quantization, context limit, active sequence count, prompt size, generated-token limit, free VRAM at startup, peak VRAM during prompt processing, and peak VRAM during decoding. Note whether the error is a CUDA allocation failure, a context-length rejection, or a process termination by the operating system. These observations distinguish a GPU-capacity problem from an input-limit or host-memory problem.
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Common mistakes to avoid
- Using the model’s maximum context by default: maximum support is not the same as a requirement for every task.
- Comparing only weight-file size with VRAM: cache, workspaces, CUDA graphs, and other processes also consume memory.
- Applying the wrong runtime flag: vLLM, Ollama, and llama.cpp expose different controls.
- Calling quantization lossless: lower precision is a trade-off, not a guaranteed quality-neutral change.
- Increasing concurrency to solve an OOM: more simultaneous sequences generally increases memory pressure.
- Buying hardware before measuring the workload: no single VRAM capacity is established as sufficient for every local model.
- Ignoring the failure stage: a model that starts with a short prompt may still fail during long-context generation or concurrent serving.
- Changing several variables at once: alter one limit, test, and record the result so you know which change helped.
- Assuming free VRAM equals usable model memory: runtime reservations and graph capture can consume part of the apparent free space.
Frequently Asked Questions
Why does my model fit when it loads but fail on a long prompt?
The prompt expands the active context and KV cache. Lower the context limit, shorten or chunk the input, and leave room for generation and runtime workspaces.
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What should I change first in vLLM?
Lower --max-model-len. If the failure appears only with multiple requests, also lower --max_num_seqs. Test each change with representative traffic.
Is 4096 the correct context size for llama.cpp?
No. NVIDIA’s example uses 4096 as an illustrative lower setting for CUDA OOM troubleshooting. Choose a value that matches your actual prompts and response needs.
Does quantization guarantee the same answer quality?
No. Quantization reduces memory by using lower precision, but its effects on quality, numerical behavior, and speed depend on the model and quantization method.
How do I free VRAM used by Ollama?
Run ollama stop <model-name> to immediately unload that resident model, then verify that the memory was released before retrying.
Why can a weight file smaller than my VRAM still fail?
Weights are only one allocation. Context state, KV cache, temporary workspaces, CUDA graphs, and other processes also consume GPU memory.
Should I buy a higher-VRAM GPU immediately?
Only after reducing context and concurrency, clearing other workloads, and testing a smaller or quantized model. Hardware must match the model, runtime, context, and simultaneous-request requirements.
Can multiple GPUs solve every local-model OOM?
No. NVIDIA documents supported profiles that distribute a model across more GPUs, but support depends on the runtime and model. Multi-GPU operation also adds hardware, power, setup, and communication costs.
The Bottom Line
Lower context length, reduce serving concurrency, clear occupied VRAM, and then try a smaller or quantized model. If that complete configuration still exceeds available memory, use a supported multi-GPU setup or hardware matched to the model and workload—not a generic VRAM target.
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