GPU × use-case guide · Speech-to-text

Is the H100 NVL a good GPU for speech-to-text?

The H100 NVL is a hopper NVIDIA GPU with 94 GB HBM3 (3938 GB/s), 835 TFLOPS BF16 / 1671 TFLOPS FP8, and a 400 W TDP. Speech-to-text workloads care most about encoder throughput and real-time factor; models are small so mid-range cards are plenty. Here's how the H100 NVL measures up.

What models fit on a single H100 NVL?

Weights only, reserving ~25% of the 94 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: Gemma 2 27B at BF16, Llama 3.3 70B at FP8, Qwen 2.5 72B at INT4. Bigger models need tensor-parallel across 8 cards.

H100 NVL for speech-to-text, specifically

Speech-to-text is throughput-bound rather than VRAM-bound, so the H100 NVL's 3938 GB/s of bandwidth and 835 TFLOPS matter more than raw capacity. Because it's latency-sensitive, run it at low batch sizes on a fast framework (vLLM ≈ 85% MFU) rather than maximising batch. Size it precisely on the calculator.

H100 NVL pricing across providers

Verdict

With 94 GB, the H100 NVL is a data-center-class card that comfortably handles speech-to-text for models up to Gemma 2 27B at full precision on a single card — a strong pick if your budget supports ~$4.1/hr.