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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThere is no single memory requirement for a local large language model (LLM). For inference, estimate the model’s weights, then add memory for its active context (the KV cache), runtime buffers, and other allocations. A model file that fits on disk—or whose weights fit in GPU memory—may still exceed the memory available when you run it.
What determines a local LLM’s memory use?
Inference memory is shaped by more than the number of model parameters. A useful mental model is:
- Weights: the stored model parameters, whose footprint depends on parameter count and precision or quantization.
- KV cache: memory for keys and values retained for the active input and generated sequence. It grows with context length and can grow with concurrent users or batch size.
- Runtime and workload overhead: activations, communication buffers, CUDA context and graphs, adapters, and any multimodal or hybrid-model reservations. Requirements vary by backend and model.
These are inference considerations; training has different memory needs and should not be estimated from the figures below.
How to estimate model-weight memory
NVIDIA’s simplified weight estimate is total parameters × bytes per parameter ÷ tensor-parallel GPU count. For one GPU, omit the division. NVIDIA’s precision guide uses 2 bytes per parameter for BF16 or FP16, 1 byte for FP8, and 0.5 byte for INT4. This is a weight estimate, not a complete running-memory budget. NVIDIA’s NIM troubleshooting guide lists additional allocations such as KV cache, activations, communication buffers, CUDA graphs, adapters, and multimodal reservations.
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Published Llama 3.1 estimates illustrate how precision changes the weight footprint. Hugging Face’s 2024 figures are checkpoint-only and exclude reserved space for kernels or CUDA graphs:
| Model | FP16 weights | FP8 weights | INT4 weights |
|---|---|---|---|
| Llama 3.1 8B | 16 GB | 8 GB | 4 GB |
| Llama 3.1 70B | 140 GB | 70 GB | 35 GB |
These figures are specific to the cited model and precision estimates; they are not universal GPU requirements. Quantization can substantially reduce weight memory, but lower precision can also reduce accuracy, and real quality and speed outcomes depend on the implementation. See Hugging Face’s Llama 3.1 guide.
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Why context length can change the answer
The KV cache holds information needed to continue processing the active sequence. Its size depends on model architecture, precision, context length, and workload. For the Llama 3.1 examples below, Hugging Face’s 2024 estimates show how much the cache grows with context; the figures are for FP16 KV cache:
| Model | 1k-token context | 16k-token context | 128k-token context |
|---|---|---|---|
| Llama 3.1 8B | 0.125 GB | 1.95 GB | 15.62 GB |
| Llama 3.1 70B | 0.313 GB | 4.88 GB | 39.06 GB |
These are model-specific estimates, not a guarantee for every runtime. NVIDIA gives a comparable example of about 40 GB of FP16 KV cache for Llama 3 70B at 128k context and batch size one; it says cache use scales linearly with the number of users. NVIDIA’s explanation of KV-cache sizing discusses that example.
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When setting a sequence limit, count both the prompt and the generated output. A long context or several concurrent requests can use substantial memory even when the model weights themselves fit.
Why a model file’s size is not its VRAM requirement
File size is useful for understanding storage, but it is not a complete runtime budget. For example, the llama.cpp README lists Llama 3.1 8B at 32.1 GB in its original form and 4.9 GB as Q4_K_M. Those are model-file examples; running inference also needs cache and other buffers. The quantized file’s size therefore should not be treated as the amount of free GPU memory required. See the llama.cpp README.
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How to check whether a setup will fit
- Identify the exact model and format. Check its parameter count, model card, and intended precision or quantized file. Different formats and runtimes can have different memory behavior.
- Estimate the weights. Apply parameter count × bytes per parameter; for tensor-parallel placement, NVIDIA’s heuristic divides by the number of GPUs used for that parallel placement.
- Set a realistic context and concurrency target. Include input plus expected output tokens, and account for batch size or concurrent users if serving requests.
- Reserve room for runtime allocations. Leave capacity for activations, buffers, CUDA context and graphs, adapters, and any multimodal state your workload uses.
- Adjust if the full workload does not fit. Reduce the configured context to the actual task’s needs. Depending on hardware and backend support, lower precision or offload and sharing options may help, but their performance and behavior are implementation-specific.
A successful model load does not prove that your target context length or serving workload will fit. Test the configuration you intend to use, including its maximum sequence length and concurrency.
What does a 24 GB GPU mean in practice?
It can be enough for some configurations, not all local LLMs. NVIDIA says Llama 3.1 8B in BF16 fits on a single 24 GB GPU with room for KV cache and overhead. That example does not make 24 GB a universal threshold: context length, runtime, and other allocations can change the result.
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Which configurations should you compare?
When choosing between model setups, compare the factors that drive the actual workload rather than relying on parameter count or file size alone:
Quick Recap
- Precision or quantization: lower precision usually reduces weight footprint, with possible quality trade-offs.
- Maximum context: longer active sequences require more KV-cache memory.
- Memory placement: check how weights and cache are distributed across available GPUs or other memory, and whether the runtime supports the arrangement.
- Concurrency and runtime: serving multiple requests and backend allocations raise the budget beyond a single model’s weights.
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