Use-case guide · Text classification

Should you pick B200 SXM for text classification?

B200 SXM has 180 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

B200 SXM fits models up to ~126B 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 B200 SXM is on the GPU detail page — click through for the sortable list.

Throughput

On text classification workloads, B200 SXM typically delivers the throughput published in its FP16 spec, minus the framework overhead (vLLM ≈ 85% MFU, TGI ≈ 70%).

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