When an open-weight model runs out of memory, first identify which allocation failed: loading the weights, creating the KV cache, capturing CUDA graphs, or running generation. Each points to a different fix. Check the error and startup logs, then change one relevant setting at a time; lowering a random memory setting can make some failures worse.
Find out what ran out of memory—and when
Confirm whether the error concerns GPU VRAM or system RAM. Then use the timing and wording in the logs to locate the failed stage. NVIDIA’s troubleshooting guide distinguishes early weight-loading failures from later KV-cache allocation errors and failures during profiling, warmup, or CUDA graph capture.
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- During model loading: the selected model and precision may require more GPU memory for weights than is available.
- After weights load, while allocating KV cache: context length and cache demand may exceed the remaining memory.
- During graph capture or warmup: the model may have loaded, but the startup stage needs additional headroom.
- After generation begins: inspect context, output length, concurrency, and other GPU workloads; the demand may change as requests run.
- On CPU RAM: check for memory pressure and swapping as well as GPU usage.
Weights are only part of the GPU budget. KV cache, activations, communication buffers, CUDA graphs, adapters, multimodal reservations, and model-specific state can also use memory. Before changing model settings, check whether another process is using the GPU. vLLM also warns that large models can consume enough CPU memory to slow the system through swapping; see its troubleshooting documentation.
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Choose a fix for the stage that failed
If the model’s weights will not load
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 can affect precision and potentially latency. Check format support for your backend and hardware before switching; the vLLM memory-conservation guide and Hugging Face Transformers optimization guide describe relevant approaches.
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If you need to retain the model, a supported setup may distribute it across multiple GPUs using tensor or pipeline parallelism. This requires compatible software and enough aggregate hardware; it does not create capacity on a single GPU.
Hugging Face’s Transformers guide gives illustrative weight-loading figures: 256GB for full-precision weights and 128GB for half-precision weights when loading a 70B Llama 2 model; for Mistral-7B-v0.1, it gives 13.74GB in half precision and 6.87GB in 8-bit. These are examples in the guide, whose publication year is not stated, accessed in 2026. They describe loading weights—not a complete runtime budget that includes cache and other allocations.
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If KV-cache allocation or generation fails
Reduce the maximum context or sequence length to what the actual prompt and expected output require. If the inference engine exposes them, reduce the number of concurrent sequences or the batch size as well. In vLLM, the documented controls include max_model_len and max_num_seqs; consult the vLLM guide for the configuration that matches your version.
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If the error points to allocator fragmentation
PyTorch may have a substantial amount of memory reserved but unallocated when an allocation fails because the allocator cannot find a sufficiently large contiguous block. For this specific case, NVIDIA documents PYTORCH_ALLOC_CONF=expandable_segments:True as a possible allocator setting. It changes allocation behavior; it does not add VRAM, and NVIDIA notes a CUDA IPC compatibility caveat. Check the NVIDIA guidance and your setup before using it.
If startup fails during graph capture or warmup
CUDA graphs use 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 failure occurs after cache allocation. Which option fits depends on the startup profile and the point of failure; see the vLLM memory guide and NVIDIA troubleshooting steps.
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If CPU memory or model loading is the bottleneck
Check system RAM use and whether the machine is swapping. vLLM notes that shared or network storage can slow model loading; local storage may help with that loading bottleneck. CPU offload is not free: it uses system memory and can add data-transfer costs. Hugging Face TRL’s memory-reduction documentation covers offloading-related techniques.
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Most model-running OOMs concern inference. Training has additional memory demands for gradients, optimizer state, and activations, so some remedies are specific to training. Hugging Face TRL documents gradient checkpointing, activation offloading, and chunked cross-entropy. Its page, checked in 2026 with no publication year stated, 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. Those figures are training-specific, and the page notes compatibility limitations; verify that a technique fits your trainer and setup before applying it.
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When to change models or add hardware
If a targeted setting change does not resolve the error, compare options against the workload rather than choosing hardware from model size alone. Check:
- Whether the weights fit at a precision supported by your backend and hardware.
- The context and output lengths the task actually needs.
- How many sequences must run concurrently, or what batch size is required.
- Whether a quantized format is compatible and whether its output quality and generation speed meet your needs.
- The cost and operational complexity of upgrading a GPU, using multiple GPUs, or moving to hosted compute.
More VRAM is appropriate when the required model, precision, cache, and runtime allocations cannot fit after sensible workload adjustments. A specific GPU recommendation depends on the target model, framework, workload, budget, location, and compatibility. Multiple GPUs can help only with a supported distribution setup and sufficient aggregate capacity; hosted compute is another option, but availability and cost depend on the provider and location.
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