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What determines a local LLM’s memory requirement?
Think of the live inference footprint as three parts: weights, KV cache, and runtime overhead. Model size and precision set the starting weight budget. Context length and concurrent requests affect cache use, while the inference backend and enabled features determine additional allocations.
- Weights: the stored model parameters, represented at a particular precision or quantization.
- KV cache: keys and values retained for the active context. It grows with context length and can grow with batch size or the number of concurrent users.
- Runtime overhead: memory for activations, communication buffers, CUDA context and graphs, adapters, and other model- or backend-specific allocations.
NVIDIA’s NIM troubleshooting documentation lists these non-weight allocations as part of GPU memory needs. A model that loads successfully may still fail at a longer context or under a busier workload.
How much memory do model weights take?
A useful first estimate is parameter count multiplied by bytes per parameter. NVIDIA’s simplified estimator divides that result by the number of GPUs used for tensor parallelism; this estimates weight memory, not the complete running process. Its precision guide assigns 2 bytes per parameter to BF16 or FP16, 1 byte to FP8, and 0.5 byte to INT4. Actual model files and runtime allocations can differ, so use the specific model and format you plan to run.
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| Example model | Precision | Estimated weight memory | Qualification |
|---|---|---|---|
| Llama 3.1 8B | FP16 | 16 GB | Checkpoint-only estimate; excludes reserved space for kernels or CUDA graphs. Hugging Face, 2024. |
| Llama 3.1 8B | FP8 | 8 GB | Checkpoint-only estimate. Hugging Face, 2024. |
| Llama 3.1 8B | INT4 | 4 GB | Checkpoint-only estimate. Hugging Face, 2024. |
| Llama 3.1 70B | FP16 | 140 GB | Checkpoint-only estimate. Hugging Face, 2024. |
| Llama 3.1 70B | FP8 | 70 GB | Checkpoint-only estimate. Hugging Face, 2024. |
| Llama 3.1 70B | INT4 | 35 GB | Checkpoint-only estimate. Hugging Face, 2024. |
These are examples, not universal requirements for every 8B or 70B model. Lower precision can substantially reduce memory use, but Hugging Face cautions that it can also reduce accuracy. Speed and quality effects depend on the model, quantization method, and implementation.
How does context length change memory use?
The KV cache retains information about tokens in the active sequence so the model can continue generating. A longer context needs more cache memory; serving more requests can increase it further. Hugging Face’s 2024 estimates for Llama 3.1 illustrate how quickly the cache can become significant:
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| Model and cache precision | 1k-token context | 16k-token context | 128k-token context |
|---|---|---|---|
| Llama 3.1 8B, FP16 KV cache | 0.125 GB | 1.95 GB | 15.62 GB |
| Llama 3.1 70B, FP16 KV cache | 0.313 GB | 4.88 GB | 39.06 GB |
The 128k figure for Llama 3 70B is also reported by NVIDIA as about 40 GB of FP16 KV cache at batch size one; NVIDIA says cache use scales linearly with user count. See its NIM troubleshooting guidance for that workload-specific example.
For a real setup, count both the prompt and generated output toward the maximum active sequence length. NVIDIA’s NIM example treats the sequence limit as input plus output tokens. A nominal context window is not the same as a promise that the full window will fit alongside the weights and runtime allocations.
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Does a quantized model’s file size equal its memory requirement?
No. Quantization reduces the weight footprint, but it does not eliminate KV cache, activations, or backend overhead. A downloaded model’s file size is therefore not a reliable estimate of total GPU memory required while generating.
For example, llama.cpp’s README lists Llama 3.1 8B at 32.1 GB in its original form and 4.9 GB for Q4_K_M. Those are model-file size examples, not complete live-inference budgets. Check the exact quantized file and runtime you intend to use, then budget separately for cache and other allocations.
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How to estimate whether your setup will fit
- Identify the exact model and format. Check its parameter count, model card, and intended precision or quantized file size. Do not assume every model in a family has the same footprint.
- Estimate weight memory. Multiply parameter count by bytes per parameter as a first approximation. For tensor-parallel placement, NVIDIA’s heuristic divides this estimate by the number of parallel GPUs.
- Set a realistic sequence limit. Include the prompt and expected output. Use a cache estimate for that context and account for batch size or concurrent users if applicable.
- Reserve space for runtime needs. Allow for activations, communication buffers, CUDA context and graphs, adapters, and any multimodal or hybrid-model state enabled by your configuration.
- Test the intended workload. Loading the checkpoint alone does not show that your target context or concurrency will fit. If cache is the constraint, reduce the configured context to suit the workload. Consider lower precision or supported offload and cache-sharing options only where your backend and hardware support them.
What does a 24 GB GPU tell you?
It is a useful example, not a universal threshold. NVIDIA says Llama 3.1 8B in BF16 fits on a single 24 GB GPU with room for KV cache and overhead. That statement applies to the specified model and configuration; a longer context, other allocations, or a different runtime can change what fits.
What memory number should you plan around?
Plan against the full workload, not a model label or download size: weights at the chosen precision, KV cache at the intended sequence length and concurrency, plus runtime overhead. Inference estimates should also be kept separate from training requirements, which are a different hardware question.
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