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Can A100 80GB SXM4 run Llama-4-Scout-17B-16E-Instruct?

NVIDIA · 80 GB HBM2e · 2039 GB/s bandwidth · Meta · 109.0B (active 17.0B) · MoE · model ctx up to 10240K

Yes — the A100 80GB SXM4 (80 GB HBM2e) can run Llama-4-Scout-17B-16E-Instruct. At Q4 with the default 8K context it needs ≈ 64.35 GB VRAM (weights 59.95 GB + KV cache 3.4 GB + 1 GB runtime overhead), which fits within 80 GB. Decode at Q4/8K: ≈ 76.3 tok/s (estimated, batch 1). FP16 needs ≈ 222.4 GB — does not fit on this card. Estimates come from memory-bandwidth math; rows tagged measured override estimates. Relative ranking is more reliable than absolute tok/s. Methodology.

Fit & speed by quant and context

QuantContextVRAM neededFits?tok/s (decode)
Q44K62.65 GBYes76.3 est.
Q48K default64.35 GBYes76.3 est.
Q432K74.55 GBYes76.3 est.
FP164K220.7 GBNo7.9 est.
FP168K default222.4 GBNo7.8 est.
FP1632K232.6 GBNo7.1 est.

Fits = weights + KV(ctx) + 1 GB overhead ≤ 95% of VRAM. Measured anchors are context-agnostic; the fit verdict is recomputed per context. A missing tok/s means the model is far beyond this card (offload-only territory).

VRAM breakdown at 8K context

QuantWeightsKV cacheOverheadTotal neededA100 80GB SXM4 VRAM
Q459.95 GB3.4 GB1 GB64.35 GB80 GB
FP16218.0 GB3.4 GB1 GB222.4 GB80 GB

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