First identify when the out-of-memory error occurs: while loading model weights, allocating the KV cache, processing a prompt, generating tokens, or capturing CUDA graphs. Those failures use memory differently, so the right fix depends on the failing stage and the inference backend. A smaller or quantized model can help when weights do not fit; a shorter context or lower concurrency can help when the KV cache is the problem.
Find the stage that runs out of memory
GPU memory is not reserved for model weights alone. It may also hold the key-value (KV) cache, runtime activations and buffers, communication buffers, CUDA graphs, adapters, multimodal reservations, or state used by hybrid models. NVIDIA’s GPU memory troubleshooting guide distinguishes weight-loading failures from KV-cache allocation failures; the remedies are not interchangeable.
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Check the complete startup log and error trace, and note whether the failure happens at startup, during prompt processing, or during generation. Before changing settings, record the serving software and version, model and precision, available GPU VRAM and system RAM, configured context length, and workload. Backend-specific options and defaults can change between releases.
- Failure while loading weights: investigate model size, precision or quantization, and whether the backend supports splitting or offloading the model.
- Weights load but cache allocation fails: investigate context length and, for a serving workload, the number of concurrent sequences.
- Trace points to CUDA graph capture or replay: test the backend’s eager or no-graph mode, if available.
- Failure occurs later in prompt processing or generation: inspect runtime allocations and workload settings rather than assuming the static weights are the only cause.
If model weights do not fit
Estimate weight memory, then leave room for runtime needs
NVIDIA’s estimate for weight memory per GPU is: total parameters × bytes per parameter ÷ tensor-parallel degree. Its documented estimate uses 2 bytes per parameter for BF16 and FP16, 1 for FP8, and 0.5 for INT4 and NVFP4. These figures estimate weights only: they do not guarantee that the full workload fits, and actual formats, kernels, overhead, and backend support matter.
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For scale, NVIDIA’s current NIM documentation estimates that a 70-billion-parameter model in BF16 needs about 140 GB for weights before other GPU allocations. Its example estimates 16 GB for Llama 3.1 8B in BF16 on one GPU; a 24 GB GPU leaves some room for the KV cache and overhead in that example, but actual fit depends on the workload. These are vendor estimates, not universal capacity guarantees. See NVIDIA’s examples and qualifications.
Choose a smaller or lower-precision model
A smaller model directly reduces weight demand. A supported quantized model can reduce it further, but format availability and performance depend on the hardware profile and backend. As the vLLM project puts it, “Quantized models take less memory at the cost of lower precision.” Check that the exact model format is supported by your installed software before switching.
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Use multiple GPUs or offload when the backend supports it
Some backends can distribute model weights across GPUs; some can also keep fewer layers on the GPU and use system memory. These approaches depend on backend controls, available hardware, and configuration, and can add setup complexity or affect performance. For example, NVIDIA estimates 35 GB of BF16 weight memory per GPU for Llama 3.3 70B split over four GPUs; other allocations still need room. In llama.cpp, review GPU-layer offload, device selection, and tensor-split controls in the server documentation. The server documents automatic fitting when relevant arguments are unset, but verify option names and defaults against your installed build.
If the KV cache cannot be allocated
The KV cache stores information used to attend to tokens in the context. A longer context can therefore require more memory beyond the model weights. Set the maximum context to what the task actually needs, rather than reserving a larger window without a workload reason. If weights load successfully but the log identifies KV-cache allocation as the failure, NVIDIA recommends lowering context length.
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For vLLM, the documented controls include max_model_len for maximum model length and, when appropriate, max_num_seqs to limit sequences served concurrently. Reducing concurrent sequences can lower serving demand, with a potential reduction in throughput. Avoid lowering gpu_memory_utilization as a supposed KV-cache fix: vLLM documents that it reduces the memory budget available for the KV cache and can make a capacity problem worse. Check the installed version’s guidance in vLLM’s memory-conservation documentation.
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CUDA graphs can use additional GPU memory. vLLM says, “By default, we optimize model inference using CUDA graphs which take up extra memory in the GPU.” If the trace implicates graph capture or replay, try vLLM’s --enforce-eager option, or the corresponding API option, as a diagnostic. This disables graph optimization and can trade inference speed for memory; it is not a general remedy for weight or KV-cache capacity problems. Consult the vLLM documentation for the installed version’s options.
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Compare fixes by the memory they address
| Option | Targets | Trade-off or dependency |
|---|---|---|
| Smaller model | Model weights | Changes the model being run; memory savings depend on the model and format. |
| Quantized model | Model weights | Lower precision; supported formats and performance depend on hardware and backend. |
| Shorter context | KV-cache demand | Reduces the available context window; set it to the task’s needs. |
| Fewer concurrent sequences | Serving and KV-cache demand | May reduce throughput; relevant concurrency controls are backend-specific. |
| Eager/no-graph mode | CUDA graph memory | Can reduce optimization and speed; use when the trace points to graph capture or replay. |
| Multiple GPUs or CPU offload | Weights on each GPU | Requires backend support and suitable hardware; can increase configuration complexity or affect performance. |
| GPU with more VRAM | Overall GPU capacity | Consider only after checking the required model, precision, backend, and workload; the needed capacity cannot be specified without those details. |
Apply one change at a time
- Save the error trace and startup log; identify the failing stage and confirm the backend and version.
- Match the fix to that stage: weights, KV cache, or CUDA graphs. Check the backend’s documentation for the exact option and current default.
- Change one relevant setting, then retry the same workload. This makes it possible to tell whether that adjustment addressed the failure.
- If the model still cannot fit, reassess model size, supported precision, context, concurrency, and multi-GPU or offload options before considering a hardware change.
A generic system-cleaning utility is not an evidenced fix for an allocation-capacity problem. Likewise, restarting or clearing memory may change what is currently allocated, but it does not make a model’s underlying weight or workload requirements smaller.
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