How much VRAM does Llama 3.1 405B need at Q4_K_M? Multi-GPU planning
Llama 3.1 405B at Q4_K_M needs roughly 324 GB of VRAM at its native 128K context. That's a highly professional datacenter configuration.
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Estimated VRAM required
324 GB
405B 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.
Hardware that fits
No single GPU in our catalog has enough memory. Multi-GPU or CPU offload required.
Just barely too small
How this is calculated
Q4_K_M weights take about 226.8 GB. KV cache adds another 67.7 GB at 128K context, with overhead pushing the total to 324 GB.
Verdict
405B local inference is achievable but not cheap. The minimum viable rig is around $30-50K of hardware, which only makes sense for serious research, regulated environments that prohibit cloud inference, or pure curiosity. For nearly every other use case, paying per-token to a hosted endpoint wins.
More Llama scenarios
Frequently asked questions
What's the minimum hardware for 405B at Q4?
Is running 405B locally worth it vs the API?
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