GPU × use-case guide · Text classification

Is the B200 NVL (pair) a good GPU for text classification?

The B200 NVL (pair) is a blackwell NVIDIA GPU with 360 GB HBM3e (16000 GB/s), 4500 TFLOPS BF16 / 9000 TFLOPS FP8, and a 1800 W TDP. Text classification workloads care most about very high request throughput on small models — this is a cost-per-1k-requests game, not a VRAM game. Here's how the B200 NVL (pair) measures up.

What models fit on a single B200 NVL (pair)?

Weights only, reserving ~25% of the 360 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: Qwen 2.5 72B at BF16, Qwen 2.5 72B at FP8, Llama 3.1 405B at INT4. Bigger models need tensor-parallel across 4 cards.

B200 NVL (pair) for text classification, specifically

Text classification is throughput-bound rather than VRAM-bound, so the B200 NVL (pair)'s 16000 GB/s of bandwidth and 4500 TFLOPS matter more than raw capacity. Because it runs offline, batch aggressively to push tokens-per-dollar down. Size it precisely on the calculator.

B200 NVL (pair) pricing across providers

Verdict

With 360 GB, the B200 NVL (pair) is a data-center-class card that comfortably handles text classification for models up to Qwen 2.5 72B at full precision on a single card — a strong pick if your budget supports ~$11.49/hr.