Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsStart with the model’s weight size, but don’t stop there: inference also uses memory for the KV cache, activations, and runtime overhead. A model file that fits on your GPU’s spec sheet is not proof that your full workload will fit. Estimate the weights, set a realistic context length, account for the other allocations, then validate the exact model and runtime.
What memory does local inference use?
For a useful estimate, separate GPU memory into four parts:
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- Model weights: the parameters loaded for inference. Parameter count and precision determine their approximate size.
- KV cache: stored keys and values for tokens already processed. It grows as a sequence is processed and generated, so longer contexts and more simultaneous sequences increase pressure.
- Activations and runtime allocations: temporary working memory, communication buffers, CUDA graphs, and other backend-specific allocations.
- Model-specific state: LoRA adapters, multimodal components, or hybrid-model state when the selected model and runtime use them.
The available capacity is what remains for the inference process after the operating system, display, and other applications have taken their share. The reviewed technical guides do not establish a universal system-RAM recommendation or a fixed percentage of GPU memory to reserve as headroom.
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NVIDIA’s first-pass per-GPU heuristic is weight_memory_per_gpu = total_parameters × bytes_per_parameter ÷ tensor_parallelism. It assumes the weights are distributed across the GPUs according to tensor parallelism; it estimates weights, not the complete runtime footprint. NVIDIA’s documentation assigns these approximate bytes per parameter to common precisions: NVIDIA NIM 2.0.13 optimization guide.
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| Weight format | Approximate bytes per parameter | Example weight estimate before other allocations |
|---|---|---|
| BF16 or FP16 | 2 | 8B parameters ≈ 16 GB |
| FP8 | 1 | 8B parameters ≈ 8 GB |
| INT4 or NVFP4 | 0.5 | 8B parameters ≈ 4 GB |
These arithmetic examples use decimal GB and the cited bytes-per-parameter heuristic; actual artifact sizes and runtime allocations vary. Quantization formats may include additional metadata or implementation-specific details, so use the exact model artifact when available rather than treating the table as a guaranteed file size.
Published figures are examples, not capacity guarantees
- Hugging Face’s inference optimization guide gives 70B Llama 2 weight examples of 256 GB at full precision and 128 GB at half precision. It also gives Mistral-7B-v0.1 examples of 13.74 GB in half precision and 6.87 GB when loaded in 8-bit. These are documented weight-memory examples, not whole-workstation recommendations: Hugging Face inference optimization guide.
- NVIDIA estimates 16 GB of BF16 weights for Llama 3.1 8B on one GPU and describes a 24 GB GPU as leaving room for KV cache and overhead in that NIM configuration. That is a configuration-specific example, not a universal minimum for every backend or context length: NVIDIA NIM 2.0.13 optimization guide.
Use quantized file size carefully
A quantized model file is a useful starting point for estimating weight storage, but it does not include every allocation made during inference. llama.cpp’s current README, accessed in 2026, lists Llama 3.1 Q4_K_M file sizes of 4.9 GB for 8B, 43.1 GB for 70B, and 249.1 GB for 405B models. The project notes that its memory and disk requirements for loading those models are the same and that adequate disk space is needed for intermediate files: llama.cpp README.
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Lower-precision weights can reduce memory use and make inference possible on more constrained GPUs. The trade-off is model- and runtime-dependent: Hugging Face notes that quantization can slightly increase latency in some cases, while llama.cpp cautions that quantization may reduce accuracy. Neither a smaller file nor a particular quantization label alone guarantees a particular speed, quality, or total VRAM requirement.
Set context length and concurrency before deciding
Choose a context that reflects the workload, including both the prompt and expected generated output. NVIDIA describes the configured maximum sequence length as covering input plus output tokens. The KV cache grows as tokens are processed, so a model that loads successfully at a short context may run out of memory at a longer one. More simultaneous sequences or a larger batch can also increase memory pressure. Cache format and model architecture affect the amount used, so there is no single reliable KV-cache figure for every model.
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When comparing setups, make the assumptions explicit rather than comparing model names alone:
- GPU memory capacity available to the inference process
- Exact model artifact, parameter count, and precision or quantization
- Target context length, including input and output, and cache format
- Batch size or number of concurrent sequences
- Inference backend and its runtime allocations
- Whether weights or other components are offloaded, or divided across GPUs
A practical estimation workflow
- Identify the exact artifact and runtime. Check the model configuration, parameter count, quantization, and the backend you intend to use. A family name such as “8B” does not specify every artifact or loading configuration.
- Estimate weights. Apply NVIDIA’s bytes-per-parameter heuristic to the parameter count, dividing by tensor parallelism when weights are distributed across GPUs. Treat this as a starting estimate, not total memory.
- Choose the real context and concurrency. Include expected prompt and output tokens, plus the number of sequences or batch settings you expect to run.
- Allow for non-weight allocations. Account for KV cache, activations, buffers, CUDA graphs, adapters, and multimodal or hybrid-model state where applicable. Their sizes depend on the model and backend.
- Use the artifact size as a cross-check. For a quantized model, its actual file size helps estimate weights, but do not equate disk size with total GPU memory required.
- Validate the complete workload. Review the runtime’s startup report or logs and test the intended context and concurrency. Leave room for measured use by the runtime and other applications; the cited documentation does not support one universal headroom percentage.
When a model does not fit
First determine what is consuming memory: weights, cache, or runtime state. If weights dominate, a smaller model or lower-precision artifact may help, subject to possible quality or latency trade-offs. If the KV cache is the constraint, reduce the context length or concurrency if the workload permits. Offloading or multi-GPU distribution may change which device bears the load, but the result depends on the backend and model; verify the actual configuration rather than assuming that parameter count predicts fit.
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