Pick the H200
When the H200 wins.
When you need 141 GB but not 4,500 TFLOPS — long-context inference, MoE serving, and most production LLM workloads cap out on memory bandwidth long before they touch the B200 ceiling.
Matched precision · same workloads · April 2026
H200 vs B200.
Hopper's last word against Blackwell's first. The B200 lands 4.5× the FP16 TFLOPS and 1.7× the memory bandwidth — for ~1.6× the cloud hourly. The question is whether your workload actually pulls on either.
§ 01 · Specs
Side by side, on paper.
Datasheet specs only. Throughput on real workloads follows in §02 — the gap there is often smaller than the FP16 number suggests, because most ML workloads are memory-bound.
| Spec | H200 | B200 |
|---|---|---|
| Vendor | NVIDIA | NVIDIA |
| Tier | Datacenter | Datacenter |
| Generation | Hopper | Blackwell |
| VRAM | 141 GB HBM3e | 192 GB HBM3e |
| Bandwidth | 4,800 GB/s | 8,000 GB/s |
| FP16 dense | 989 TFLOPS | 4,500 TFLOPS |
| TDP | 700 W | 1000 W |
| Released | 2024 | 2025 |
| Status | Available | Limited |
| Price | ~$3.70/hr cloud | ~$6+/hr cloud |
Fig 2 · Spec deltas. Copper dot marks the column with the bigger number for that axis (lower W is better; otherwise higher).
§ 02 · Benchmarks
On real workloads.
Same model revision, same quantisation, same batch size on both cards. Where one side has no measurement we leave the cell empty rather than extrapolate.
Methodology: how we test.
| Category | Workload | Metric | H200 | B200 | Δ |
|---|---|---|---|---|---|
| LLM Inference | Llama 3.1 8B | tok/s | 280 | 540 | 1.93× |
| LLM Inference | Llama 3.1 70B · 4-bit | tok/s | 78 | 165 | 2.12× |
| LLM Inference | Qwen 2.5 32B · 4-bit | tok/s | 95 | 200 | 2.11× |
| LLM Inference | Mistral 7B | tok/s | 320 | 620 | 1.94× |
| Image Generation | SDXL 1024×1024 | it/s | 11.2 | 22 | 1.96× |
| Image Generation | Flux.1 Dev | it/s | 5.9 | 12 | 2.03× |
| Training | Fine-tune Llama 3.1 8B LoRA | samples/s | 26 | 52 | 2.00× |
| Training | ResNet-50 · ImageNet | img/s | 5,800 | 11,500 | 1.98× |
| Computer Vision | YOLOv8x · inference | FPS | 580 | 1,100 | 1.90× |
| Computer Vision | SAM ViT-H | masks/s | 16.5 | 32 | 1.94× |
| Audio/Video | Whisper Large v3 | × RT | 52 | 100 | 1.92× |
Fig 3 · Δ column shows B200 ÷ H200 on the same workload. Copper dot marks the winner per row.
§ 03 · Verdict
When each one wins.
The right card is the one whose envelope covers your worst-case workload — not the one with the bigger TFLOPS number.
Pick the B200
When the B200 wins.
When the step is FLOP-bound: large-batch training, dense matmul kernels, and ResNet-style throughput. B200 also wins outright on availability of FP4/FP8 paths if your stack is current.
Bottom line. For most teams in 2026 the H200 is the right answer at $3.70/hr — B200 only pays back when your training is FLOP-saturated and your scheduler keeps it that way.
§ 04 · More head-to-heads
Other matchups, same format.
/hardware/h100-vs-h200
H100 SXM vs H200 →
Same FP16 ceiling (989), but H200 nearly doubles VRAM (80 → 141 GB) and 1.4× bandwidth.
/hardware/rtx-5090-vs-rtx-4090
RTX 5090 vs RTX 4090 →
Blackwell vs Ada. 32 GB GDDR7 against 24 GB GDDR6X, at 1.27× the FP16.
/hardware/rtx-5090-vs-h200
RTX 5090 vs H200 →
The biggest consumer card vs a real datacenter accelerator. When does the 5090 actually catch up?
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