GPU × use-case guide · Reasoning / agentic

Is the GH200 a good GPU for reasoning / agentic?

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. Reasoning / agentic workloads care most about long output generations (chain-of-thought), so decode throughput and KV-cache headroom dominate. 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 reasoning / agentic, specifically

Reasoning / agentic 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'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 reasoning / agentic for models up to Gemma 2 27B at full precision on a single card — a strong pick if your budget supports ~$3.49/hr.