When an open-weight model runs out of memory, first identify which stage failed: loading weights, allocating the KV cache, generating output, or capturing CUDA graphs. Each points to a different fix. Check the error and startup logs, change one relevant setting at a time, then retry; lowering a memory setting indiscriminately can make some failures worse.
Find when the out-of-memory error occurs
Start with the error message and logs. Confirm whether the exhausted resource is GPU VRAM or system RAM, and note what the program was doing when it failed. NVIDIA’s troubleshooting guide distinguishes failures during weight loading, KV-cache allocation, and graph capture or warmup; the right remedy depends on the stage (NVIDIA’s memory troubleshooting guide).
- During model loading: The weights, at the selected precision, may exceed available GPU memory.
- After weights load, during KV-cache or block allocation: The model’s context and cache demand may exceed the remaining budget.
- During graph capture, profiling, or warmup: Startup needs additional headroom for that stage.
- After generation begins: Check whether longer sequences or concurrent requests increase the memory demand.
Weights are only one part of runtime memory. The GPU may also need memory for the KV cache, activations, communication buffers, CUDA graphs, adapters, multimodal reservations, or model-specific state. Before changing model settings, check whether other workloads are using the GPU and whether the host is running short of RAM.
Choose a fix that matches the failure
If the model fails while loading weights
Try a smaller model or a lower-memory precision or quantized variant supported by both the model and your inference backend. Quantization reduces the memory used to store weights, but may affect precision or latency. Check the backend and hardware documentation for support for the exact format you plan to use (vLLM memory-conservation guidance; Hugging Face Transformers optimization guide).
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For scale, Hugging Face gives illustrative weight-loading figures for a 70B Llama 2 model: 256GB for full-precision weights and 128GB for half-precision weights. Its examples for Mistral-7B-v0.1 are 13.74GB in half precision and 6.87GB in 8-bit. These are the guide’s model-weight loading examples, not complete runtime memory estimates or universal hardware recommendations; the page’s publication year is not stated (Hugging Face Transformers optimization guide, accessed 2026).
If you must keep the model, a supported configuration may split it across multiple GPUs using tensor or pipeline parallelism. This requires compatible software and enough aggregate hardware; it does not add capacity to a single GPU. vLLM documents tensor parallelism as an option for splitting a model across GPUs (vLLM memory-conservation guidance).
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If KV-cache allocation or generation fails
Reduce the maximum context or sequence length to what the prompt and output actually require. Also reduce batch size or the number of concurrent sequences if your inference engine provides those controls. In vLLM, relevant settings include max_model_len and max_num_seqs; check the documentation for the version you have installed because configuration details can change (vLLM memory-conservation guidance).
A long default context can require more KV-cache memory than remains after loading weights and making other allocations. In NVIDIA’s documented KV-capacity failure case, lowering --gpu-memory-utilization can shrink the cache budget and make the failure worse. Do not assume that reducing a utilization value always fixes an OOM; follow the guidance for the specific failure stage (NVIDIA’s memory troubleshooting guide).
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If the error indicates allocator fragmentation
PyTorch may show a large amount of reserved memory that is not allocated. This can indicate that the allocator could not find a sufficiently large contiguous block for a request. NVIDIA documents PYTORCH_ALLOC_CONF=expandable_segments:True as a possible allocator setting for this case. It changes allocator behavior; it does not increase the GPU’s memory capacity, and NVIDIA notes a CUDA IPC compatibility caveat. Check the guide and your setup before applying it (NVIDIA’s memory troubleshooting guide).
If startup fails during graph capture or warmup
CUDA graphs consume GPU memory. vLLM documents adjusting graph-capture sizes or setting enforce_eager=True to disable graph capture. NVIDIA also describes reducing the cache allocation budget to leave more room when the failure occurs after cache allocation. Which change fits depends on the profile and the point where startup stops; consult the framework’s current configuration guidance (vLLM memory-conservation guidance; NVIDIA’s memory troubleshooting guide).
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If system RAM or model loading is the bottleneck
Check host RAM use and whether the system is swapping. vLLM warns that model loading can consume substantial CPU memory; shared or network storage can also slow loading, for which local storage may help (vLLM troubleshooting guidance). CPU offload is not free: it uses system memory and can add data-transfer costs (Hugging Face TRL memory guidance).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep inference fixes separate from training fixes
Most attempts to run an open-weight model are inference, but training has additional memory demands from gradients, optimizer state, and activations. Hugging Face TRL documents gradient checkpointing, activation offloading, and chunked cross-entropy for training; these are not direct drop-in fixes for a standard inference OOM.
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TRL reports that its chunked cross-entropy path typically lowers peak VRAM by about 30%, and by up to about 50% in specified Qwen3-1.7B/FSDP2 configurations. The page checked in 2026 does not state a publication year, and it notes compatibility limitations. Treat those figures as documentation for those training configurations, not a general inference estimate (Hugging Face TRL memory guidance).
Decide whether a model change or hardware change is needed
If one targeted change does not solve the problem, compare options against the workload you actually need to run:
- Will the weights fit at a precision supported by your backend and hardware?
- What context and output lengths does the task require?
- How many sequences must run concurrently, or what batch size is needed?
- Does the quantization format work with the model, backend, and hardware?
- Does the memory-saving change preserve acceptable output quality and generation speed?
- Would a GPU upgrade, multiple GPUs, or hosted compute make sense given total cost and operational complexity?
A GPU with more VRAM is relevant when the diagnosis shows a genuine capacity limit, but the model, workload, format, budget, and location determine what would fit. The official configuration guides describe individual options; they do not establish one universally best model, GPU, or backend. Their online documentation and flags can change, so check the current guidance for your installed software version (NVIDIA; vLLM; Hugging Face Transformers).
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