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First identify when the failure happens: while loading model weights, allocating the context’s KV cache, processing a prompt or generating tokens, or capturing/replaying CUDA graphs. Each stage uses memory differently, so the right fix depends on the error trace and the inference backend—such as vLLM or llama.cpp. A shorter context can help a KV-cache failure, but it will not make oversized model weights fit.
Find the stage where memory runs out
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, and state for hybrid models. NVIDIA’s GPU memory troubleshooting guide distinguishes weight-loading failures from KV-cache allocation failures; the remedies differ.
Before changing settings, note the serving software and version, model and precision, GPU VRAM and system RAM, configured context length, and whether the failure occurs at startup, prompt processing, or generation. Read the startup log and error trace to locate the allocation that failed. Change one relevant setting at a time, then retry the same workload.
- Failure during weight loading: The model’s weights, or the selected way of placing them, do not fit in the available GPU memory.
- Failure allocating KV cache: The weights loaded, but the runtime cannot reserve enough memory for context or active sequences.
- Failure around CUDA graph capture or replay: The trace implicates a runtime optimization that uses additional GPU memory.
- Failure during prompt processing or generation: Inspect the trace and workload settings; runtime buffers, activations, context, or concurrent requests may be relevant.
If model weights do not fit
Estimate weight memory before changing the workload
NVIDIA gives this estimate for weight memory per GPU: total parameters × bytes per parameter ÷ tensor-parallel degree. Its documented estimates use 2 bytes per parameter for BF16 and FP16, 1 byte for FP8, and 0.5 byte for INT4 and NVFP4. These are weight estimates, not guarantees that the full workload will fit; cache and runtime allocations need additional memory, and actual format, kernels, backend support, and overhead matter.
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For scale, NVIDIA estimates that an 8-billion-parameter Llama 3.1 model in BF16 takes 16 GB for weights on one GPU. Its example says a 24 GB GPU leaves room for KV cache and overhead, but whether a particular workload fits still depends on its settings. NVIDIA also estimates about 140 GB of BF16 weight memory for a 70-billion-parameter model before other GPU allocations; its example of Llama 3.3 70B split over four GPUs gives 35 GB of weights per GPU. These are vendor-published estimates, not capacity guarantees.
Choose a smaller or lower-precision model
If the weight estimate exceeds the available capacity, use a smaller model or a supported quantized/lower-precision version. Quantization reduces weight memory, but trades away numerical precision; practical support and performance depend on the hardware, model profile, and backend. Check that the exact format is supported by the software serving the model rather than assuming every format works on every GPU.
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Use more than one GPU or offload layers where supported
A backend may be able to distribute model work across multiple GPUs, or keep fewer layers on the GPU and use system memory for the rest. These are backend-specific options and can add setup complexity or affect performance. In llama.cpp, review the server’s GPU-layer offload, device selection, and tensor-split controls; its server documentation describes automatic fitting when arguments are unset. Verify option names and defaults for the installed build in the llama.cpp server documentation.
If KV-cache allocation fails
When weights load but the runtime cannot allocate KV cache, reduce the maximum context length to what the task actually needs. Longer context and more active sequences require additional memory beyond static weights. NVIDIA recommends lowering context length for KV allocation failures, and vLLM documents context and sequence limits as memory controls.
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- Reduce context length: In vLLM, lower
max_model_lento the smallest value that supports the intended prompts and outputs. For other backends, use the corresponding context-length setting for that version. - Reduce serving concurrency if needed: In vLLM, lower
max_num_seqswhen the workload allows fewer sequences to be active at once. This trades throughput or concurrency for a lower memory demand. - Retry the same prompt and serving pattern: If the allocation now succeeds, increase context or concurrency only as far as the workload requires and the available memory permits.
Do not lower vLLM’s gpu_memory_utilization expecting it to solve a KV-capacity shortage: lowering it reduces the memory budget available for the KV cache and can make that failure worse. See the project’s memory-conservation documentation for the controls and tradeoffs.
If the trace points to CUDA graphs
vLLM says CUDA graphs use extra GPU memory. If the trace indicates graph capture or replay, test eager execution as a diagnostic: start vLLM with --enforce-eager, or use the corresponding API option. If that changes the failure, graph optimization is implicated; keeping it disabled may conserve memory but gives up that optimization and can affect inference speed. Confirm the option against the installed vLLM version and consult its memory-conservation guidance.
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Compare the remedies by what they change
| Remedy | Memory target | Main tradeoff or requirement |
|---|---|---|
| Smaller model | Model weights and, depending on model and workload, total runtime demand | Changes model capability; select a model suited to the task. |
| Lower-precision or quantized model | Primarily weight memory | Reduced numerical precision; format support and performance depend on backend and hardware. |
| Shorter context | KV cache | Limits the prompt and generation context the runtime can support. |
| Fewer active sequences | Serving demand, including KV-cache use | Reduces concurrency or throughput; settings are backend-specific. |
| Eager mode instead of CUDA graphs | Graph-related GPU memory | Gives up graph optimization and may affect speed; relevant when the trace points to graph capture or replay. |
| Multi-GPU distribution or CPU offload | GPU weight placement | Requires backend and hardware support; can increase setup complexity or affect performance. |
When a hardware change is justified
Consider a GPU with more VRAM only after identifying the model, precision, backend, and workload and trying the relevant software controls. If the desired model and required context or concurrency still exceed available memory, additional capacity may be necessary. The information given here is not enough to recommend a particular GPU: fit depends on the model, its format, the serving software, the rest of the system, and how the model will be used.
A restart or system-cleaning utility is not a general fix for a workload whose memory requirements exceed available capacity. Start with the failed allocation in the log and adjust the setting that controls it.
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