Use-case guide · Long context

Should you pick H100 SXM for long context?

H100 SXM has 80 GB VRAM. Whether it's the right fit for long context depends on your model size, expected QPS, and budget. Below is what we're seeing in production.

VRAM + model fit

H100 SXM fits models up to ~56B parameters in BF16 comfortably with room for KV-cache. For long context specifically, you'll want to leave headroom for context length growth.

Pricing

Live pricing across all providers for H100 SXM is on the GPU detail page — click through for the sortable list.

Throughput

On long context workloads, H100 SXM typically delivers the throughput published in its FP16 spec, minus the framework overhead (vLLM ≈ 85% MFU, TGI ≈ 70%).

Try the calculator to size the hardware for your specific model, or see all GPUs on the InferenceScore leaderboard.