The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →A model can fit on a GPU as weights and still run out of memory when you use a 100K-token context. The weights are only one part of inference memory: the runtime also needs room for the prompt’s key-value (KV) cache, activations, communication buffers, CUDA graphs and other allocations. Without the failed run’s logs and measurements, it is not possible to say which allocation caused a particular out-of-memory error. The way to find out is to identify when it failed and account for what was already using VRAM.
Why can a model load but fail at 100K tokens?
Loading a model establishes that its weights fit under the conditions of that load. It does not prove that the GPU can also hold the KV cache and runtime allocations needed for a particular prompt length and workload. NVIDIA’s troubleshooting guidance lists KV cache, activations, communication buffers and CUDA graphs among the additional memory consumers.
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During inference, the KV cache stores attention keys and values for tokens the model is processing and, during generation, for tokens it has generated. Its requirements depend on the model and runtime configuration, as well as the amount of context being processed and how many sequences are active. A long configured context can therefore require more cache than remains after weights and other allocations are accounted for. A model’s advertised context window is not a guarantee that a given GPU can serve that many tokens.
The failure phase offers an important clue. An error during loading or startup points to a different allocation point than an error while processing a prompt (prefill) or generating tokens (decode). Inference engines can allocate memory differently, so the phase narrows the investigation but does not, on its own, identify the cause.
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How much VRAM do the weights use?
As a first estimate, NVIDIA gives this formula for weight memory per GPU:
total parameters × bytes per parameter ÷ tensor-parallel degree
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NVIDIA’s stated estimates use 2 bytes per parameter for BF16 or FP16, 1 byte for FP8 and 0.5 bytes for INT4. This is an estimate of weight storage per GPU, not a complete inference-memory budget. Actual runtime needs must also accommodate cache and other allocations.
| Documented example | Weight-memory estimate | What it does and does not establish |
|---|---|---|
| Llama 3.1 8B, BF16, one GPU | 16 GB | NVIDIA says this leaves room on a 24 GB GPU for KV cache and overhead. It does not establish that every context length or runtime configuration will fit. |
| Llama 3.3 70B, BF16, four GPUs | 35 GB per GPU | NVIDIA’s arithmetic is 70 billion parameters × 2 bytes ÷ 4. This is a weight estimate, not the total runtime requirement. |
| Llama 3.3 70B, FP8, two GPUs | 35 GB per GPU | NVIDIA’s example shows how lower-precision weights can change the device count. Cache and runtime requirements still need to fit. |
These are NVIDIA documentation examples, not measurements of a particular 100K-token run. Compare a weight estimate with the memory actually used after loading; do not treat it as the GPU’s full bill.
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How to find what caused the out-of-memory error
- Capture the failure phase and exact error. Record whether the error occurred during model loading or startup, prompt processing, or generation. Save the framework logs around the failure rather than relying on the point at which the interface reported it.
- Measure VRAM at each stage. Record free memory before launch, after weights load, and at the failure point. Note other processes using the GPU. This shows whether memory was already occupied before inference and how much the run added.
- Write down the weight configuration. Record the model and parameter count, weight precision or quantization, and tensor-parallel degree. Compare the resulting estimate with observed memory after loading.
- Record the context and workload. Note the configured maximum context, actual prompt length, KV-cache dtype or size if exposed by the runtime, and the number of concurrent sequences. A large maximum context may affect the cache budget even when a particular request is shorter, depending on runtime and configuration.
- Inspect runtime reservations and overhead. Check the installed engine’s GPU memory-utilization, KV-cache and CUDA-graph settings, along with its logs. Settings and defaults are version-sensitive; use documentation matching the installed version.
- Change one setting at a time. Test a shorter context, fewer concurrent sequences, lower weight precision, a deliberate cache budget, or a different distribution across devices. Record whether the workload fits and any effect on quality, speed or throughput.
This process distinguishes evidence from inference: if memory rises sharply during prefill, cache or activation demand is worth investigating; if the run fails before the prompt is processed, inspect startup allocations and what was already resident. Neither pattern proves a particular cause without the measurements.
Which changes can reduce memory demand?
| Change | Memory component affected | Trade-off or caution |
|---|---|---|
| Use lower-precision or quantized weights | Reduces weight storage. Hugging Face’s Transformers documentation describes quantization as an inference memory optimization. | Evaluate model quality and runtime support for the chosen quantization. The documentation cited here does not quantify the quality impact for a specific model or workload. |
| Reduce maximum context length | Can reduce the cache budget required by the configured workload. | Limits how much input context the application can handle. |
| Reduce concurrent sequences | Can lower the memory demand of simultaneous requests. | Reduces concurrency and may limit throughput. |
| Set cache size or GPU utilization deliberately | Controls how much memory the runtime makes available or reserves for model operations and cache. | A higher reservation is not automatically safer: vLLM warns that setting GPU memory utilization too high can cause OOM. Its API documents explicit KV-cache sizing; when kv_cache_memory_bytes is provided, it takes precedence over gpu_memory_utilization. |
| Disable CUDA graph capture where appropriate | Can avoid the additional GPU memory used by CUDA graphs. | vLLM’s memory-conservation guidance notes that disabling graph capture can reduce inference throughput. |
| Distribute weights across more GPUs or offload some to CPU | Can ease the amount of weight memory required on an individual GPU. | Consider the additional hardware, interconnect and speed implications. No universal performance result follows from the documentation. |
| Use a GPU with more VRAM | Adds capacity when measured demand exceeds available GPU memory. | First establish that capacity is the binding limit. NVIDIA’s 24 GB example is specific to its 8B BF16 weight estimate plus room for cache and overhead; it is not a promise that another model will fit at 100K tokens. |
How to interpret GPU-memory settings
In vLLM, gpu_memory_utilization is documented as the fraction of GPU memory used for model operations, including weights, activations and KV cache. Choosing a larger value can leave more memory available to the runtime, but it also leaves less headroom for other allocations and can cause OOM. The API also documents kv_cache_memory_bytes for explicit KV-cache sizing; when set, it takes precedence over the utilization setting.
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These controls are not universal recipes. Check the documentation for the installed runtime version and compare the configured budget with actual free VRAM and other GPU users. A setting copied from a different engine, version or workload may not produce the same result.
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What can be concluded about a 100K-token failure?
The memory model explains why the error can happen: weights may load successfully while the cache and runtime allocations required for a long context exceed the remaining VRAM. But the prompt length alone does not reveal whether the limiting allocation was KV cache, activations, graph capture, other resident processes or a combination. Naming the culprit in an individual run requires its configuration, logs and memory measurements at the point of failure.
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