GPU × use-case guide · Summarisation

Is the H100 NVL 94GB (per GPU pair) a good GPU for summarisation?

The H100 NVL 94GB (per GPU pair) is a hopper NVIDIA GPU with 188 GB HBM3 (7876 GB/s), 1670 TFLOPS BF16 / 3341 TFLOPS FP8, and a 800 W TDP. Summarisation workloads care most about large context windows and batch throughput — documents are long and jobs run offline, so cost-per-token beats latency. Here's how the H100 NVL 94GB (per GPU pair) measures up.

What models fit on a single H100 NVL 94GB (per GPU pair)?

Weights only, reserving ~25% of the 188 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: Llama 3.3 70B at BF16, Qwen 2.5 72B at FP8, Qwen 2.5 72B at INT4. Bigger models need tensor-parallel across 4 cards.

H100 NVL 94GB (per GPU pair) for summarisation, specifically

Summarisation is context-heavy, so the KV cache — not the weights — is what fills the 188 GB. On the H100 NVL 94GB (per GPU pair) you'll trade context length against batch size: long prompts mean fewer concurrent requests. Because it runs offline, batch aggressively to push tokens-per-dollar down. Size it precisely on the calculator.

H100 NVL 94GB (per GPU pair) pricing across providers

Verdict

With 188 GB, the H100 NVL 94GB (per GPU pair) is a data-center-class card that comfortably handles summarisation for models up to Llama 3.3 70B at full precision on a single card — a strong pick if your budget supports ~$7.49/hr.