Can H100 80GB SXM5 run Llama-4-Scout-17B-16E-Instruct?
NVIDIA · 80 GB HBM3 · 3350 GB/s bandwidth ·
Meta · 109.0B (active 17.0B) · MoE · model ctx up to 10240K
Yes — the H100 80GB SXM5 (80 GB HBM3) 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: ≈ 125.4 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
Quant
Context
VRAM needed
Fits?
tok/s (decode)
Q4
4K
62.65 GB
Yes
125.4est.
Q4
8K default
64.35 GB
Yes
125.4est.
Q4
32K
74.55 GB
Yes
125.4est.
FP16
4K
220.7 GB
No
12.9est.
FP16
8K default
222.4 GB
No
12.7est.
FP16
32K
232.6 GB
No
11.7est.
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
Quant
Weights
KV cache
Overhead
Total needed
H100 80GB SXM5 VRAM
Q4
59.95 GB
3.4 GB
1 GB
64.35 GB
80 GB
FP16
218.0 GB
3.4 GB
1 GB
222.4 GB
80 GB
Other GPUs that run Llama-4-Scout-17B-16E-Instruct