How much VRAM does Llama 3.1 70B need at FP16? Full-precision requirements
Llama 3.1 70B at FP16 needs roughly 201.2 GB of VRAM at its native 128K context. That's a massive datacenter-only configuration: multiple high-end cards pooled together.
Calculator
Estimated VRAM required
201 GB
70B params at FP16, 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
Just barely too small
How this is calculated
FP16 weights are exactly 140 GB. KV cache adds another 43 GB at 128K context, plus 18 GB of activation overhead, totaling 201.2 GB.
Verdict
FP16 70B is the reference point, not the deployment target. Use it to validate quantized variants against, then switch to Q8_0 or Q4_K_M for everything you actually serve. The 4x cost saving is real, the quality loss is not.
More Llama scenarios
Frequently asked questions
Why would I run Llama 3.1 70B at FP16?
What's the cheapest hardware that runs FP16 70B?
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