GPU × use-case guide · Vision-language

Is the GH200 a good GPU for vision-language?

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. Vision-language workloads care most about extra VRAM for the vision encoder and image tokens on top of the language model. 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 vision-language, specifically

Vision-language is context-heavy, so the KV cache — not the weights — is what fills the 96 GB. On the GH200 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.

GH200 pricing across providers

Verdict

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