When an LLM hits a CUDA out-of-memory error, first identify what it was doing at the moment it failed. Loading model weights, allocating inference KV cache, training, and capturing CUDA graphs all have different memory demands—and different fixes. Check the error and GPU usage before changing settings; clearing the cache or buying a larger GPU may not address the actual cause.
Diagnose when and where the OOM occurs
Record the exact error message and the operation that failed. For a serving process, inspect startup logs to see whether the error occurs while loading weights, allocating the KV cache, or compiling and warming up CUDA graphs. NVIDIA treats these as distinct failure phases, so the timing helps narrow down the remedy.
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Check total GPU memory and which processes are using it. Also distinguish memory actively allocated by your program from memory reserved by a framework allocator: PyTorch notes that unused memory held by its caching allocator can still appear as used in nvidia-smi. PyTorch’s memory profiler also cannot see every allocation; memory obtained directly through CUDA APIs or other libraries, including NCCL, may be outside its view.
Do not assume every OOM is fragmentation. If the model, cache, and workload genuinely need more memory than the GPU has, allocator settings cannot create additional VRAM. Fragmentation is worth investigating when the error and memory statistics show substantial reserved-but-unallocated memory or inactive split blocks.
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If the model will not fit while loading
Estimate weight storage before changing hardware. NVIDIA’s rule of thumb is total parameters × bytes per parameter ÷ tensor parallelism. Its table assigns two bytes per parameter to BF16 and FP16, and one byte per parameter to FP8. This estimates weights only—not KV cache, activations, communication buffers, CUDA graphs, or other runtime overhead.
| NVIDIA example | Estimated weight memory | What it means |
|---|---|---|
| 8-billion-parameter Llama 3.1, BF16, one GPU | 16 GB | NVIDIA’s NIM guide says this example fits on a 24 GB GPU with room for KV cache and overhead. It is an example, not a guarantee for other runtimes or workloads. |
| 70-billion-parameter Llama 3.3, BF16, four GPUs | 35 GB per GPU | Illustrates the estimated weight allocation when distributed across four GPUs. |
| 70-billion-parameter Llama 3.3, FP8, two GPUs | 35 GB per GPU | Illustrates that a lower-precision format does not necessarily mean less memory per GPU when the GPU distribution also changes. |
These are NVIDIA figures from its current NIM troubleshooting guide, accessed in 2026; they are estimated weight requirements, not independent benchmark results or universal hardware requirements. See NVIDIA’s GPU memory troubleshooting guide.
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If weights are the bottleneck, consider a supported lower-precision or quantized profile, distributing the model across more GPUs, or using a smaller model. Confirm that the exact model and serving runtime support the format and distribution you plan to use. Lower precision can affect output quality, and splitting a model across GPUs requires compatible hardware and runtime support.
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Inference memory is not just model weights. The KV cache stores information used to generate tokens and grows with inference needs such as context length and concurrent requests. Review the serving stack’s context limit, batching or concurrency, and cache budget. Reducing these demands can lower live memory use, though it may limit how many requests or how much context the server can handle.
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For NVIDIA NIM with vLLM, the --gpu-memory-utilization setting controls the budget for model operations; NVIDIA documents a default of 0.9. Check the documentation for your installed version before changing or copying a command, since this is a runtime-specific setting rather than a universal PyTorch option.
If logs show failure during KV-cache allocation alongside considerable reserved-but-unallocated memory, allocator fragmentation may be a factor. For the NIM/PyTorch context covered by NVIDIA, the guide documents PYTORCH_ALLOC_CONF=expandable_segments:True as a possible remedy. Use it only when the symptoms and runtime match; it is not a general fix for workloads that exceed physical capacity.
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If training reaches its memory peak
Training often uses substantial memory for activations as well as weights. Reduce the micro-batch size or sequence length to reduce how much work is resident at once. If the training loop supports it, gradient accumulation can use smaller micro-batches while preserving a larger effective batch; check the framework’s loss scaling and optimizer-step behavior when changing it.
Activation checkpointing is another trade-off. It saves fewer intermediate activations and recomputes them during the backward pass, reducing activation memory at the cost of additional compute. PyTorch describes the technique in its guide to activation checkpointing.
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If CUDA graph capture or warmup fails
A serving process can load its model and allocate cache successfully, then run out of memory during graph capture or warmup because those stages need additional headroom. For NVIDIA NIM, the troubleshooting guide recommends lowering --gpu-memory-utilization to leave more memory unreserved, or disabling CUDA graphs with the NIM option or eager-mode flag documented for that runtime. Disabling graphs can reduce inference throughput. Do not apply NIM flags to another server or a general PyTorch setup without checking that runtime’s documentation.
What torch.cuda.empty_cache() does—and does not do
PyTorch says that torch.cuda.empty_cache() “Releases all unoccupied cached memory currently held by the caching allocator so that those can be used in other GPU applications and visible in nvidia-smi.” In other words, it releases unused cached blocks for other applications and can make memory reporting clearer; it does not increase the memory available to your active PyTorch workload.
If the active workload still needs more memory than is available, address live allocations instead: remove references to objects you no longer need and reduce model, cache, or workload demand. For details, see PyTorch’s CUDA semantics documentation and its CUDA memory usage guide.
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When a GPU with more VRAM makes sense
Consider a GPU upgrade if a supported smaller or lower-precision configuration, reduced context or concurrency, and workload tuning still cannot meet your needs. Match capacity to the whole workload: model weights, KV cache, activations, communication buffers, CUDA graphs, and runtime overhead. An 8B BF16 example fitting on a 24 GB GPU does not establish that 24 GB is sufficient for every model, server, or workload.
A “GPU with 24GB VRAM” is a capacity category, not a specific card recommendation. Before choosing a GPU, check its current price and availability, board dimensions, power requirements, cooling, compatibility, and fit for your intended model and runtime.
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