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A model fitting on your GPU as weights does not mean a 100K-token prompt will fit. The weights are only one part of inference memory: the KV cache grows with context, while activations, runtime buffers and CUDA graphs also need space. Without logs from the failing run, it is not possible to identify which allocation caused a particular error—but you can determine it by checking when the failure occurs and what memory was already in use.
Why can a model load and still run out of GPU memory?
Loading a model establishes that its weights could be placed in memory; it does not prove that the GPU can also hold the cache and runtime allocations needed for a particular prompt and workload. NVIDIA identifies KV cache, activations, communication buffers and CUDA graphs as additional consumers of GPU memory. The available amount can also be reduced by other GPU processes.
A useful first-pass estimate for weight memory per GPU is:
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NVIDIA’s estimate uses 2 bytes per parameter for BF16 or FP16, 1 byte for FP8 and 0.5 byte for INT4. These figures estimate weights, not total inference memory. Actual allocation can vary with model and runtime.
Examples of weight estimates
NVIDIA’s undated troubleshooting examples estimate 16 GB for Llama 3.1 8B at BF16 on one GPU. It notes that this leaves room on a 24 GB GPU for KV cache and overhead, but does not guarantee a given context or runtime will fit. For Llama 3.3 70B at BF16, the guide estimates 35 GB per GPU across four GPUs (70 billion parameters × 2 bytes ÷ 4). At FP8, it estimates 35 GB per GPU across two GPUs. Runtime memory still needs to be budgeted in each case.
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See NVIDIA’s GPU memory troubleshooting guide for the weight estimates and memory-allocation discussion.
What does the KV cache do at 100K tokens?
During generation, an inference engine stores key and value data from tokens so it can reuse that information rather than recomputing the entire preceding sequence at each step. The KV cache therefore consumes GPU memory alongside the model weights. Longer contexts require more cache capacity, and concurrent sequences add to the workload’s cache needs.
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NVIDIA warns that a model’s default context length can require more KV-cache memory than remains after weights, activations and overhead are accounted for. An advertised context window is not a promise that a model will run at that length on every GPU, precision, or serving setup.
The practical question is not just whether the prompt is 100K tokens, but whether the runtime’s configured context, actual input, cache representation and concurrency fit in the GPU memory left after other allocations.
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How do I find which allocation caused the OOM?
Start with the failure phase. A startup or model-load error points to a different moment in the allocation sequence than an error during prompt processing (prefill) or token generation (decode). The distinction helps narrow what to inspect; allocation behavior differs across inference engines, so a phase alone does not prove the cause.
- Record the exact failure. Save the complete error and logs, and note whether it occurs during startup, prefill or decode.
- Measure memory at each point. Record free VRAM before launch, after weights load and at the failure. Note other processes using the GPU.
- Write down the model profile. Record the model and parameter count, loading precision or quantization, GPU model and count, and tensor-parallel degree. Compare the weight estimate with the observed allocation rather than treating the estimate as the whole memory bill.
- Capture the workload. Record the configured maximum context, actual prompt length, cache type or dtype, and number of concurrent sequences.
- Inspect runtime settings. Note the installed engine and version, GPU memory-utilization setting, explicit cache sizing, and CUDA graph settings. Use documentation for that version rather than assuming a setting has identical behavior across releases.
- Change one variable per test. After establishing a baseline, test a shorter context, fewer concurrent sequences, lower weight precision, a different cache budget or another runtime profile. Record whether the run fits and any effect on quality, latency or throughput.
Which changes can reduce the memory demand?
Each mitigation targets a different part of the budget. The best choice depends on whether the shortfall comes from weights, cache, runtime overhead or concurrent work.
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| Option | What it changes | Trade-off or caution |
|---|---|---|
| Use lower-precision or quantized weights | Reduces weight storage; documented formats include FP8 and INT4 as well as BF16/FP16. | Model quality and runtime support need evaluation. The cited documentation does not quantify the quality impact for a specific model. |
| Shorten maximum context | Reduces the cache capacity required for the supported sequence length. | Longer prompts or conversations may no longer fit. |
| Reduce concurrent sequences | Lowers memory demand from simultaneous requests and their caches. | Reduces the workload that can be served concurrently. |
| Set cache and utilization budgets deliberately | Controls the memory allocated or reserved for model operations and KV cache. | A high GPU-utilization fraction can itself cause OOM. In vLLM, explicit kv_cache_memory_bytes takes precedence over gpu_memory_utilization when supplied; consult the installed version’s documentation. |
| Disable CUDA graph capture where appropriate | Can reduce memory used by graph capture. | vLLM warns that disabling graph capture can reduce inference throughput. |
| Distribute the model across more GPUs or offload weights to CPU | Moves some weight capacity away from a single GPU. | May add hardware, interconnect or speed costs; the sources do not establish a universal performance result. |
For implementation-specific details, consult vLLM’s API documentation on memory utilization, explicit KV-cache sizing and CPU offload; vLLM’s memory-conservation guide on context, sequence count and CUDA graphs; and Hugging Face Transformers’ quantization documentation on reducing weight memory. These controls and defaults are version-sensitive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When is more VRAM the right fix?
Consider a GPU with more memory only after accounting shows that capacity is the binding limit and workload reductions or supported precision and cache settings are unsuitable. A specific weight estimate cannot establish that a different model will fit at 100K tokens: the cache, runtime and concurrency still need room.
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