How much VRAM does Llama 3.1 8B need at Q4_K_M? Single GPU local inference
Llama 3.1 8B at Q4_K_M needs about 23.8 GB of VRAM at its native 128K context. This is what makes local inference of small models at high context highly attractive on 24 GB consumer GPUs.
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Estimated VRAM required
23.8 GB
8B params at Q4_K_M, 131,072 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA (DeepSeek). Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
KV cache exceeds model weights: Consider lowering the context length to save on VRAM. Contexts between 8K and 64K are generally more typical for local setups.
Hardware that fits
How this is calculated
8B at Q4_K_M is roughly 4.5 GB of weights plus 17.2 GB of KV cache and 2.1 GB of activation overhead, totaling 23.8 GB.
Verdict
If you have a working PC, you can run Llama 3.1 8B locally. Q4_K_M is the recommended quant - it fits everywhere and the quality loss vs FP16 is negligible at this scale. Step up to Q8_0 only if you have headroom and want maximum quality.
More Llama scenarios
Frequently asked questions
Can I run Llama 3.1 8B on a 6 GB GPU?
Is Llama 3.1 8B good enough for daily use?
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