GPU × use-case guide · Text-to-speech

Is the GH200 a good GPU for text-to-speech?

The GH200 is a hopper NVIDIA GPU with 96 GB HBM3 (4000 GB/s), 990 TFLOPS BF16 / 1980 TFLOPS FP8, and a 900 W TDP. Text-to-speech workloads care most about low-latency streaming synthesis; small models, so value cards win. Here's how the GH200 measures up.

What models fit on a single GH200?

Weights only, reserving ~25% of the 96 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

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

GH200 for text-to-speech, specifically

Text-to-speech is throughput-bound rather than VRAM-bound, so the GH200's 4000 GB/s of bandwidth and 990 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.

GH200 pricing across providers

Verdict

With 96 GB, the GH200 is a data-center-class card that comfortably handles text-to-speech for models up to Gemma 2 27B at full precision on a single card — a strong pick if your budget supports ~$3.49/hr.