GPU × use-case guide · Information extraction
Is the B200 SXM a good GPU for information extraction?
The B200 SXM is a blackwell NVIDIA GPU with 180 GB HBM3e (8000 GB/s), 2250 TFLOPS BF16 / 4500 TFLOPS FP8, and a 1000 W TDP. Information extraction workloads care most about structured-output throughput at scale; latency matters less than tokens-per-dollar. Here's how the B200 SXM measures up.
What models fit on a single B200 SXM?
Weights only, reserving ~25% of the 180 GB for KV cache, activations and fragmentation. ✓ = fits on one card.
| Model | BF16 | FP8 | INT4 |
|---|---|---|---|
| Llama 3.1 8B | ✓ | ✓ | ✓ |
| Qwen 2.5 14B | ✓ | ✓ | ✓ |
| Gemma 2 27B | ✓ | ✓ | ✓ |
| Mixtral 8x7B (MoE) | ✓ | ✓ | ✓ |
| Llama 3.3 70B | — | ✓ | ✓ |
| Qwen 2.5 72B | — | ✓ | ✓ |
| Llama 3.1 405B | — | — | — |
Largest single-card fit: Mixtral 8x7B (MoE) at BF16, Qwen 2.5 72B at FP8, Qwen 2.5 72B at INT4. Bigger models need tensor-parallel across 8 cards.
B200 SXM for information extraction, specifically
Information extraction is throughput-bound rather than VRAM-bound, so the B200 SXM's 8000 GB/s of bandwidth and 2250 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 SXM pricing across providers
| Provider | On-demand $/hr | Reserved $/hr |
|---|---|---|
| lambda | $5.99 | $4.49 |
| runpod | $7.2 | — |
| coreweave | $7.5 | $5.5 |
Verdict
With 180 GB, the B200 SXM is a data-center-class card that comfortably handles information extraction for models up to Mixtral 8x7B (MoE) at full precision on a single card — a strong pick if your budget supports ~$5.99/hr.
See full B200 SXMspecs & pricing, size your model on the calculator, or compare every GPU on the GPU list.