If a local LLM runs out of memory after you increase its context window, the requested context and workload may exceed the memory available to its runtime. In vLLM, start by lowering max_model_len and, if serving multiple sequences, max_num_seqs. Then review model quantization, GPU memory budgeting, and offload options. These settings are vLLM-specific; check the documentation for your installed runtime before applying them elsewhere.
Why does a larger context window cause an out-of-memory error?
A context window is not a free setting: the runtime must reserve memory to process and retain the tokens in a request. In vLLM, the GPU memory budget covers model weights, activations, and the key-value (KV) cache. Increasing the configured context can leave too little room for those components or for other work using the device.
The memory required depends on the model, prompt length, runtime configuration, concurrency, and hardware. There is no universal safe context length or VRAM calculator established by the vLLM documentation. A setting that works for one model or workload may fail for another.
How to troubleshoot the error in vLLM
- Confirm the runtime and setting. Identify the application, model, and exact context limit that triggers the error. The configuration names below apply to vLLM; do not assume they are valid in Ollama, llama.cpp, or another runtime. Check that runtime’s documentation and the documentation for your installed version.
- Lower the context limit. In vLLM, reduce
max_model_lento the smallest value that supports your task. Test that configuration, then raise it gradually if it runs reliably. vLLM lists context length as a memory-control setting in its memory-conservation guide. - Reduce the number of concurrent sequences. If multiple requests or sequences are being served, lower
max_num_seqs. This reduces concurrent workload and is another memory-reduction measure documented by vLLM’s memory-conservation guide. - Consider a quantized model. vLLM says quantized models use less memory, with lower precision as the tradeoff, and documents static and dynamic quantization paths. The effect on output quality depends on the model and quantization; the guide does not provide a universal quality or memory estimate.
- Review the GPU memory budget and KV cache. The vLLM LLM API reference describes
gpu_memory_utilizationas the share of GPU memory reserved for weights, activations, and KV cache, and warns that setting it too high may cause OOM. It also documentskv_cache_memory_bytesfor more direct cache sizing. Tune these against the actual device and workload rather than simply maximizing the values. - Check whether CUDA graph capture is using memory you need. vLLM says CUDA graphs use additional GPU memory and documents
enforce_eagerto disable graph capture. This is an option to evaluate, not a guaranteed fix; execution behavior and performance may change. - Evaluate CPU or multi-GPU placement if supported. The API documents
cpu_offload_gbfor offloading model weights to CPU memory, but CPU-GPU transfers occur during each forward pass. Tensor parallelism can split a model across GPUs. Whether either option helps depends on the available hardware, runtime, and workload. - For multimodal requests, check the media inputs. If you use a multimodal model, vLLM documents limits for image, video, and audio inputs and notes that disabling unused modalities can reduce memory use. This step is relevant only when the model or request includes media.
CPU weight offload and KV-cache offload are different
Weight offload moves some model weights to CPU memory. In vLLM, cpu_offload_gb is the documented control for this approach, and the API notes the CPU-GPU transfer cost during each forward pass.
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KV offloading instead stores completed KV-cache blocks in a slower, larger memory tier, including CPU host memory, and brings them back to the GPU as needed. vLLM describes this separately in its KV Offloading Usage Guide. Both approaches trade transfer time for memory capacity. Check support and configuration against your installed vLLM release.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When is more hardware worth considering?
Consider additional GPU memory or multiple GPUs only after checking the context limit, concurrency, model memory, cache budget, and available runtime features. More capacity may help when the configured model and workload do not fit, but the sources do not support a particular GPU recommendation without knowing the model, current hardware, budget, and performance needs.
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When comparing possible remedies, consider what memory each one affects—weights, activations, KV cache, or multimodal inputs—as well as precision or quality, inference speed and transfer overhead, and configuration complexity. The vLLM documentation describes these tradeoffs but does not provide a universal numerical comparison across runtimes.
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