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CUDA out-of-memory (OOM) errors are fixed by matching the remedy to the allocation that failed. If the log stops while loading weights, the model and precision need more VRAM; if it fails while creating the KV cache, reduce context length or concurrency; if PyTorch has much more memory reserved than allocated, address allocator fragmentation; and if failure occurs during CUDA-graph capture or warm-up, leave more headroom or use eager execution. Read the failure phase first, then change one variable at a time so you know which fix worked.
This guide explains how to diagnose each phase, estimate weight memory, reduce runtime and training usage, configure PyTorch’s allocator, and decide when a larger GPU is justified.
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What does a CUDA out-of-memory error mean for a local LLM?
A local LLM uses GPU memory for more than its model file. NVIDIA’s startup model divides memory into weights, the key-value (KV) cache, activations, communication buffers, and CUDA graphs. A card can therefore load the weights successfully and still fail when a request, cache, or graph is created.
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The wording and timing of the error are more useful than the headline “CUDA out of memory.” A failure during weight loading is a capacity problem. A failure during KV-cache allocation is usually a context or concurrency problem. A large “reserved but unallocated” value points to fragmentation. A graph-capture or warm-up failure means earlier allocations left too little contiguous headroom.
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How much memory do the model weights need?
For a first estimate, NVIDIA gives this heuristic:
weight_memory_per_gpu = total_parameters × bytes_per_parameter ÷ tensor_parallelism
The approximate bytes per parameter are 2 for BF16 or FP16, 1 for FP8, and 0.5 for INT4 or NVFP4. These figures cover weights only; reserve additional VRAM for the KV cache and runtime overhead.
| Precision | Approximate bytes per parameter | Weight estimate for a 70B model before overhead |
|---|---|---|
| BF16 or FP16 | 2 | About 140 GB |
| FP8 | 1 | About 70 GB |
| INT4 or NVFP4 | 0.5 | About 35 GB |
For example, NVIDIA notes that a 70-billion-parameter model in BF16 needs approximately 140 GB just for weights, so it cannot fit on one 80 GB GPU. Quantization can make the weights fit, but it does not eliminate KV-cache or framework overhead.
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How do I identify which allocation caused the OOM?
Read the final lines of the runtime log and note what the program was doing immediately before the exception. The same model may need different fixes for different phases.
| Failure phase | What it usually means | First changes to try |
|---|---|---|
| Weight loading or model initialization | Weights at the selected parameter count and precision do not fit in the available VRAM. | Use a smaller model, lower precision or quantization, or add tensor/pipeline parallelism. |
| KV-cache allocation, request admission, or first long prompt | The maximum input-plus-output sequence length or simultaneous requests needs more cache than the runtime budget allows. | Lower maximum context length and request concurrency. |
| Allocation after a long run with “reserved” far above “allocated” | Free blocks exist, but fragmentation prevents a sufficiently large contiguous allocation. | Try expandable allocator segments and remove unnecessary long-lived allocations. |
| CUDA graph capture or warm-up | The model loaded, but capture and its temporary buffers need additional headroom. | Lower the configured GPU-memory budget or disable CUDA graphs and run eagerly. |
Hugging Face shows a representative message such as: “CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch).” Compare total capacity, allocated memory, reserved memory, and the requested allocation instead of treating “free” as the only number that matters.
How do I fix an OOM while loading model weights?
Weight-loading OOM means the selected model representation cannot fit before normal inference begins. Reduce the weight footprint or distribute it; changing prompt length will not solve this phase.
Choose a smaller parameter count
Move to a model with fewer parameters when the task allows it. This reduces weight memory and usually lowers compute and latency, but may reduce reasoning ability, language coverage, or tool-use capability. Check the parameter count of the exact checkpoint and whether it loads in the precision you selected.
Use lower precision or quantization
BF16 and FP16 use about 2 bytes per parameter in NVIDIA’s estimate. FP8 uses about 1 byte, while INT4 or NVFP4 uses about 0.5 byte. Lower precision can make a model fit, but quality depends on the model, quantizer, calibration, and workload; validate important tasks rather than assuming all quantized checkpoints behave alike.
Spread the model across GPUs
Tensor parallelism divides weight memory by the tensor-parallel degree in the heuristic. Pipeline parallelism can also place different layers on different GPUs. Configure the parallelism supported by your inference server, then verify that every GPU has enough room for its share plus its own cache and communication buffers. Two cards with the same nominal capacity can still fail if one has less free memory or if the split is unbalanced.
Leave room beyond the arithmetic estimate
The bytes-per-parameter calculation is not a capacity guarantee. Runtime kernels, temporary buffers, communication, the KV cache, and graph capture all consume VRAM. Treat the result as a lower bound and keep a practical margin rather than targeting 100 percent utilization.
How do I fix an OOM caused by context length or the KV cache?
If weights load but the server fails while reserving the KV cache or when a long request arrives, lower the maximum sequence length and the number of simultaneous requests. The KV cache grows with the maximum input-plus-output sequence length and with active requests, so a context setting that works for one request may fail under concurrency.
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Lower the runtime’s maximum model length, context window, or input-plus-output token limit. This reduces the largest KV-cache reservation even when most real prompts are short. Do not confuse the model’s advertised context window with what your GPU can serve; the hardware budget may support only a smaller operational limit.
Reduce request concurrency
Lower the server’s maximum concurrent sequences, batch size for inference, or equivalent admission limit. Throughput may fall because fewer requests are processed together, but each admitted request has more cache headroom. If a queue is available, queue excess requests instead of allowing them to trigger another cache allocation.
Control output length as well as input length
The relevant sequence is input plus generated output. A short prompt with a very high maximum-new-tokens value can reserve more cache than expected. Set an output cap appropriate to the application and test the longest realistic prompt and completion together.
Retest under realistic load
A single short request does not prove that a context setting is safe. Exercise the maximum prompt, maximum output, and intended concurrency after each change. Record peak allocated and reserved memory so a later configuration change does not silently consume the remaining margin.
How do I fix CUDA OOM during training?
Training needs memory for activations and gradients in addition to weights and optimizer state. The fastest first step is to lower per_device_train_batch_size; Hugging Face recommends gradient accumulation when you need to preserve the effective batch size.
Lower the per-device batch
Set a smaller batch on each GPU until a training step fits. A lower per-device batch reduces activation memory immediately, but it also changes how often gradients are synchronized unless you compensate with accumulation.
Use gradient accumulation
Accumulate gradients over several smaller micro-batches before updating weights. For example, reducing a per-device batch from 8 to 2 and accumulating four steps can preserve an effective batch of roughly 8 while lowering the peak memory of each forward and backward pass. The exact effective batch also depends on the number of devices and any distributed-training settings.
Stop retaining autograd graphs
Long-lived statistics, Python lists, callbacks, and logging code can accidentally retain tensors that still have autograd history. PyTorch recommends detaching values, or accessing underlying data when appropriate, before storing them beyond the training step. Store a scalar such as a detached loss value rather than the full tensor that produced it.
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Find leaks that appear only after many steps
An OOM that occurs after hundreds of successful steps often indicates retained references rather than a model that is simply too large. Inspect lists of losses or outputs, validation caches, hooks, and custom metrics. Clear references at the end of their intended lifetime and rerun with the same number of steps to confirm that peak memory stabilizes.
What does “reserved but unallocated” memory mean in PyTorch?
“Allocated” is memory currently assigned to live tensors. “Reserved” includes blocks held by PyTorch’s caching allocator for future allocations, including blocks that are currently unused. A large gap between reserved and allocated can indicate fragmentation: enough total memory exists, but it is split into blocks that cannot satisfy the next contiguous request.
Try expandable allocator segments
Set the allocator configuration before starting the process:
export PYTORCH_ALLOC_CONF=expandable_segments:TruePyTorch documents PYTORCH_CUDA_ALLOC_CONF as the backward-compatible alias:
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:TrueUse one spelling in a given deployment and restart the process so the setting is read during initialization. This option mainly addresses allocation behavior; it does not create additional VRAM and will not make an oversized model fit.
Reduce allocation churn
Repeatedly changing batch shapes, context lengths, or temporary tensor sizes can leave a fragmented pool. Prefer stable shapes where your serving stack permits it, avoid creating unnecessary large temporaries, and release references to tensors that are no longer needed. Restarting the worker can clear a badly fragmented pool, but treat that as recovery rather than a capacity fix.
How do I fix OOM during CUDA graph capture or warm-up?
Graph capture can require a temporary memory peak after weights and caches are already allocated. NVIDIA’s documented NIM example says CUDA graph capture can consume 1–5 GB. If loading succeeds but warm-up or capture fails, lower the configured GPU-memory budget so the runtime reserves headroom, or disable CUDA graphs and execute eagerly.
Lower the runtime GPU-memory budget
Reduce the serving framework’s memory-utilization, fraction, or equivalent budget setting. The exact label varies by runtime, but the purpose is the same: prevent weights and cache reservations from consuming every byte before capture begins. Retest warm-up after each reduction.
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Use the runtime’s eager-execution option if a lower budget still cannot leave enough space. Eager execution can reduce throughput or increase latency because operations are launched normally instead of replaying a captured graph, but it avoids the capture-time allocation peak. Keep the change scoped to the workload that needs it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which CUDA OOM fix should I try first?
Choose the smallest change that addresses the observed phase. The trade-offs differ: some changes affect quality, some reduce throughput, and allocator settings mainly improve how memory is packed.
| Remedy | Memory reduction or capacity gain | Quality or capability effect | Latency/throughput effect | Scope and effort |
|---|---|---|---|---|
| Smaller model | Large reduction in weight and runtime memory. | May reduce capability, reasoning, or coverage. | Often faster, but model-dependent. | Changes the workload; requires checkpoint and prompt validation. |
| Lower precision or quantization | About 2 bytes/parameter in BF16/FP16, 1 in FP8, or 0.5 in INT4/NVFP4 under NVIDIA’s heuristic. | Can reduce output quality; verify on representative tasks. | May improve memory bandwidth, but kernel support determines speed. | Changes the model representation; moderate implementation effort. |
| Shorter context | Reduces maximum KV-cache reservation. | Shortens the recall window. | Can improve latency and permit more requests. | Usually one serving configuration; easy to test. |
| Lower concurrency or batch | Reduces simultaneous KV cache or activation memory. | No direct quality loss per response. | Reduces throughput and may increase queueing. | Usually one workload; easy operational change. |
| Gradient accumulation with smaller micro-batches | Lowers training-step peak activation memory. | Preserves effective batch when configured correctly. | More steps per optimizer update; training can take longer. | Training configuration change. |
| Allocator configuration | Can recover usable capacity lost to fragmentation. | None by itself. | Usually little direct effect; behavior is workload-dependent. | Process-level setting; restart required. |
| Eager execution instead of CUDA graphs | Avoids graph-capture peak, not the base model footprint. | None to model output. | May reduce throughput or increase latency. | Serving-runtime setting. |
| Larger GPU | Adds physical VRAM capacity. | None to model output. | May improve speed, but depends on the GPU and workload. | Highest cost and affects the whole machine. |
What is the safest step-by-step troubleshooting procedure?
Use this sequence to avoid changing unrelated settings and to preserve a known-good baseline.
- Capture the exact failure. Save the final log lines, the model checkpoint, precision, context limit, concurrency or batch setting, GPU model, and free VRAM immediately before launch.
- Classify the phase. Mark the error as weight loading, KV-cache allocation, fragmentation-sensitive allocation, training activation growth, or graph capture/warm-up.
- Apply one phase-specific change. For weights, reduce parameters or precision or add parallelism. For KV cache, reduce context and concurrency. For training, lower per-device batch and use accumulation. For fragmentation, set expandable segments. For graph capture, lower the memory budget or switch to eager execution.
- Restart the process. Environment variables and many allocator settings are read only at startup; a restart also removes stale allocations from the previous run.
- Test the worst supported case. Use the longest realistic prompt plus output, intended concurrency, or the largest training batch you plan to run. A successful short smoke test is not sufficient.
- Measure the result. Compare peak allocated and reserved memory, latency, throughput, queueing, and output quality. Keep the change only if it solves the OOM without violating the workload’s requirements.
- Escalate capacity last. If software and workload reductions still leave no margin, move to a GPU with more VRAM or distribute the model across GPUs.
When is upgrading to a higher-VRAM GPU justified?
Upgrade when the model and required workload genuinely exceed the available capacity after you have addressed precision, context, concurrency, training batch, fragmentation, and graph capture. A larger card is the most direct fix for a capacity limit, but it is also the most expensive and does not correct a memory leak or an unreasonable context setting.
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How can I prevent the next CUDA OOM?
- Document a tested maximum context length, output limit, and concurrency instead of relying on the model’s advertised context window.
- Keep a VRAM margin for KV cache, temporary buffers, communication, and graph capture; do not configure the model to consume the card’s entire capacity.
- Record precision, quantization, tensor or pipeline parallelism, and allocator settings with each deployment.
- Load-test the longest realistic prompt and output at the intended concurrency before production use.
- Monitor allocated and reserved memory over time to detect fragmentation or a retained autograd graph.
- For training, log per-device batch size, accumulation steps, and peak memory so a dataset or sequence-length change does not create a surprise.
Frequently Asked Questions
Can clearing the CUDA cache permanently fix an out-of-memory error?
Cache-clearing can release unused cached blocks, but it cannot make weights, KV cache, activations, or graph buffers smaller. If the same allocation fails after a clean restart, change the workload or model configuration rather than repeatedly clearing the cache.
Why does the same model fit in one local LLM application but not another?
Applications can use different precisions, KV-cache data types, batching policies, graph settings, and memory allocators. Compare those settings and the maximum sequence and concurrency limits, not just the model name and GPU capacity.
Will reducing context length change the model’s answers?
It can: tokens outside the new limit are unavailable, so long-document recall and conversation history may be reduced. For short prompts that remain within the limit, response quality is usually unaffected by the lower ceiling.
How do I know whether my GPU is running out of VRAM or system RAM?
A CUDA OOM names the GPU allocator and reports device capacity, allocated memory, or reserved memory. System RAM exhaustion produces a different operating-system or framework error; monitor both resources because CPU offloading can shift pressure from VRAM to RAM.
Should I use FP8 or INT4 for every local model to avoid OOM?
No. Lower precision reduces memory, but support, speed, and quality vary by model and GPU. Choose the lowest precision that passes your accuracy and latency tests, and retain enough memory for the KV cache and runtime overhead.
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