GPU × use-case guide · Information extraction
Is the H200 SXM a good GPU for information extraction?
The H200 SXM is a hopper NVIDIA GPU with 141 GB HBM3e (4800 GB/s), 990 TFLOPS BF16 / 1979 TFLOPS FP8, and a 700 W TDP. Information extraction workloads care most about structured-output throughput at scale; latency matters less than tokens-per-dollar. Here's how the H200 SXM measures up.
What models fit on a single H200 SXM?
Weights only, reserving ~25% of the 141 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.
H200 SXM for information extraction, specifically
Information extraction is throughput-bound rather than VRAM-bound, so the H200 SXM's 4800 GB/s of bandwidth and 990 TFLOPS matter more than raw capacity. Because it runs offline, batch aggressively to push tokens-per-dollar down. Size it precisely on the calculator.
H200 SXM pricing across providers
| Provider | On-demand $/hr | Reserved $/hr |
|---|---|---|
| lambda | $3.49 | $2.69 |
| tensordock | $3.8 | — |
| coreweave | $4.25 | $3.19 |
| runpod | $4.69 | — |
Verdict
With 141 GB, the H200 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 ~$3.49/hr.
See full H200 SXMspecs & pricing, size your model on the calculator, or compare every GPU on the GPU list.