There is no single VRAM requirement for running a local language model. The main factors are the model and checkpoint, weight precision or quantization, context length, runtime, and other GPU workloads. Model-file size is a useful starting point—not a guarantee that the model will fit or run well. For inference, estimate the weights first, then allow room for the rest of the workload.
Start with the model’s weights, then add runtime headroom
A quick first estimate is parameter count multiplied by the bytes used for each parameter. Lenovo’s inference-sizing guide expresses this as M = P × Z × 1.2, where P is the parameter count in billions, Z is the precision factor in bytes, and 1.2 adds an estimated 20% overhead. Its factors are 0.5 bytes for INT4, 1 byte for FP8 or INT8, 2 bytes for FP16, and 4 bytes for FP32. This is a rough estimate, not a promise: actual memory also depends on the checkpoint, context, runtime, and workload. Lenovo’s inference sizing guide
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Checkpoint file sizes help make the estimate concrete. The llama.cpp project README lists the following original and Q4_K_M sizes for Llama 3.1:
| Model | Original size | Q4_K_M size |
|---|---|---|
| Llama 3.1 8B | 32.1 GB | 4.9 GB |
| Llama 3.1 70B | 280.9 GB | 43.1 GB |
| Llama 3.1 405B | 1,625.1 GB | 249.1 GB |
These are model-file or quantized-checkpoint sizes, not universal VRAM requirements. A runtime needs memory beyond the weights, and the context and other processes can raise the total. Check the exact checkpoint and runtime guidance before treating a file size as a fit test.
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What published VRAM examples do—and don’t—tell you
NVIDIA’s NIM for LLMs version 1.7.0 gives rough memory guidelines of about 15 GB for Llama 8B, 131 GB for Llama 70B, 14 GB for Mistral 7B Instruct v0.3, and 88 GB for Mixtral 8x7B Instruct v0.1. These figures apply to NVIDIA’s NIM guidance, not every local runtime or quantized checkpoint. NVIDIA cautions that actual requirements can be lower or higher depending on hardware and NIM configuration. NVIDIA NIM for LLMs, version 1.7.0
Documented examples from other sources are similarly specific. Hugging Face’s Transformers optimization documentation describes a setup using more than 15 billion parameters that required 32 GB in its documented configuration, 15 GB at 8-bit, and just over 9 GB at 4-bit. Those figures describe that example, not a general requirement for models of that size. Hugging Face Transformers optimization documentation
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Why the same model can need different amounts of VRAM
Precision and quantization
Lower-bit weights usually take less memory, which can make a larger model usable on a given GPU. But quantization is a tradeoff: Hugging Face notes that it can affect accuracy and, in some cases, inference time. Its documented 4-bit example ran more slowly than the 8-bit example. Evaluate output quality and speed for your own task rather than assuming the smallest file is automatically the best choice. Hugging Face Transformers optimization documentation
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The model’s weights are only part of the memory budget. A longer prompt or generation context can add memory demand; Hugging Face explains that attention-related memory pressure increases with sequence length. Decide how much context you actually need before sizing a setup. A short-context, single-user workload and a long-context or multi-process workload should not be treated as equivalent.
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Architecture, runtime, and concurrent work
Parameter count is useful, but architecture and implementation matter. Mixture-of-experts models and different runtime backends can complicate a simple estimate. Backend support, GPU architecture and memory, model format, API needs, target throughput, and simultaneous GPU processes all affect the practical choice. NVIDIA recommends considering these factors when selecting an inference backend. NVIDIA NIM hardware support guidance
Some sizing guidance also includes memory reserved for the operating system, containers, or other processes. For example, NIM’s estimates are tied to its configuration; do not transplant allowances from that setup to an unrelated runtime. Leave practical headroom, but use guidance for the software and model you intend to run.
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Inference is not the same as fine-tuning
The estimates above address inference: loading a model to generate outputs. Fine-tuning or training is a different memory problem. Lenovo’s guide gives substantially higher estimated requirements for full fine-tuning than for inference, while LoRA and QLoRA can reduce requirements depending on method and precision. Do not use an inference estimate as a training estimate. Lenovo’s inference and fine-tuning sizing guide
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How to check whether a model fits your setup
- Identify the exact model and checkpoint. Find its parameter count, format, and file size; distinguish an original checkpoint from a quantized one.
- Choose the precision or quantization. Compare the expected memory reduction with the possible effect on output quality and speed.
- Set a realistic context and workload. Account for prompt and generation length, concurrent users or processes, and any other GPU work.
- Check the runtime’s requirements for that combination. Use its guidance for the exact model format, backend, GPU, and configuration; treat general formulas and vendor tables as estimates.
- Allow headroom and validate in practice. If the workload does not fit, try a smaller model or lower-bit checkpoint, reduce context, or consider CPU/system-memory offload. Offload can allow some setups to load a model that exceeds VRAM, but it is not the same as keeping the workload in GPU memory and can affect performance.
How to compare GPU options
Compare candidate setups against the work you intend to do, not VRAM capacity in isolation. Check the exact model and checkpoint, quantization, context length, memory headroom, supported runtime, and expected speed or throughput. The available guidance does not establish a tested ranking of consumer GPUs or a universal card capacity that suits everyone. A GPU with more VRAM may expand the models or contexts you can run, but whether that is worthwhile depends on your target workload and budget.
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