Use-case guide · Text classification

Should you pick GH200 for text classification?

GH200 has 96 GB VRAM. Whether it's the right fit for text classification depends on your model size, expected QPS, and budget. Below is what we're seeing in production.

VRAM + model fit

GH200 fits models up to ~67B parameters in BF16 comfortably with room for KV-cache. For text classification specifically, you'll want to leave headroom for context length growth.

Pricing

Live pricing across all providers for GH200 is on the GPU detail page — click through for the sortable list.

Throughput

On text classification workloads, GH200 typically delivers the throughput published in its FP16 spec, minus the framework overhead (vLLM ≈ 85% MFU, TGI ≈ 70%).

Try the calculator to size the hardware for your specific model, or see all GPUs on the InferenceScore leaderboard.