Short answer: NVIDIA H100 SXM provides 80GB HBM3 on Hopper; B200 provides 180GB HBM3E on Blackwell. B200 more than doubles nominal single-device capacity and raises published bandwidth from 3.35TB/s to up to 8TB/s, but it also moves to compute capability 10.0 and a newer software-validation boundary. Choose from the exact workload: Qwen3.5-122B 4-bit and Q8 artifacts can fit by files on one B200, while an 80GB H100 is only a tight file boundary for the listed 4-bit artifacts and requires multiple devices for FP8, Q8 and BF16.
This comparison uses H100 SXM and B200 SXM. H100 also exists as an 80GB passive PCIe card and a 94GB H100 NVL PCIe product. B200 is described here in its eight-GPU HGX/DGX platform context. Do not compare an H100 PCIe listing with an HGX B200 baseboard as though they were interchangeable cards.
NVIDIA H100 vs B200 specifications
| Specification | H100 SXM | B200 SXM |
|---|---|---|
| Architecture | Hopper / GH100 | Blackwell / GB100 |
| Compute capability | 9.0 | 10.0 |
| Memory per GPU | 80GB HBM3 | 180GB HBM3E |
| Published memory bandwidth | 3.35TB/s | Up to 8TB/s |
| GPU-to-GPU bandwidth | 900GB/s | Up to 1.8TB/s |
| Eight-GPU aggregate memory | 640GB across separate devices | 1.44TB across separate devices |
| Maximum configurable power | Up to 700W | Up to 1,000W |
| Multi-instance GPU | Up to seven MIG instances | Up to seven MIG instances |
What fits: Qwen3.5-122B-A10B
| Artifact | Files | H100 80GB | B200 180GB |
|---|---|---|---|
| GPTQ 4-bit | 78.864GB | Fits by files; only 1.136GB margin | Fits; 101.136GB margin |
| Q4 plus projector | 77.446GB | Fits by files; only 2.554GB margin | Fits; 102.554GB margin |
| Official FP8 | 127.163GB | No; 2 × 80GB arithmetic minimum | Fits; 52.837GB margin |
| Q8 plus projector | 130.781GB | No; 2 × 80GB arithmetic minimum | Fits; 49.219GB margin |
| Official BF16 | 250.173GB | No; 4 × 80GB arithmetic minimum | No; 2 × 180GB arithmetic minimum |
The H100 4-bit rows are file-size boundaries, not credible runtime guarantees. A remainder of 1–3GB cannot safely absorb CUDA context, cache, hybrid-attention state, temporary buffers, projector/vision processing and concurrency. B200 changes FP8 and Q8 into single-device file fits, but those larger margins still require measured peak-memory validation.
What fits: Qwen3.5-397B-A17B
| Artifact | Files | H100 80GB minimum by files | B200 180GB minimum by files |
|---|---|---|---|
| GPTQ 4-bit | 235.708GB | 3 GPUs total 240GB leaves only 4.292GB; plan 4 | 2 × 180GB |
| Q4 plus projector | 245.012GB | 4 × 80GB | 2 × 180GB |
| Official FP8 | 406.152GB | 6 × 80GB | 3 × 180GB |
| Q8 plus projector | 422.426GB | 6 × 80GB | 3 × 180GB |
| Official BF16 | 806.796GB | 11 × 80GB | 5 × 180GB |
Arithmetic device counts are not deployment recommendations. GPU memories remain separate even with NVLink/NVSwitch, and the runtime must place or shard weights, cache and workspaces with headroom on every device. The H100 three-device GPTQ total is another file-size trap: 240GB exceeds the files by only 4.292GB before runtime allocation.
When B200 changes the decision
- 122B FP8 or Q8: B200 turns a two-H100 arithmetic requirement into a one-device file fit.
- 122B 4-bit: both fit by files, but H100 leaves almost no runtime headroom while B200 leaves more than 100GB.
- 397B: B200 roughly halves several arithmetic device counts, but still requires explicit parallel placement.
- Workloads already comfortable below 80GB: capacity alone does not justify migration. Measure the exact workload and total platform cost.
Software generation is part of the comparison
H100 is Hopper with compute capability 9.0; B200 is Blackwell with compute capability 10.0. A working H100 container does not prove that its framework build, quantization kernels, communication libraries or compiled extensions include the Blackwell target. Validate the exact host driver, CUDA toolkit, container, framework, serving runtime and model-specific kernels on the candidate B200 system.
MIG and aggregate memory can mislead
NVIDIA lists up to seven MIG instances for both H100 and B200. A process assigned a MIG profile sees that partition, not the physical GPU’s full 80GB or 180GB. Similarly, eight-GPU totals of 640GB and 1.44TB are aggregate capacity across separate devices, not transparent device-local pools. Record MIG state, exposed memory, topology and placement in every capacity or benchmark claim.
Complete-system benchmark checklist
- Identify the exact H100 variant or B200 HGX/DGX system SKU.
- Record GPU count, memory exposed per accelerator, MIG state and NVLink/NVSwitch topology.
- Pin driver, CUDA, container, framework, kernels and model revision.
- Hold model artifact, precision, context, batch/concurrency, placement, offload and quality target constant.
- Measure peak per-device memory, time to first token, prompt and generation throughput, throttling and wall power.
- Compare complete acquisition, support, cooling and operating cost—not bare accelerator names.
Plan the next step
- Review H100 PCIe, SXM and NVL variants
- Review the B200 capacity guide
- Compare H100 and H200
- Compare H200 and B200
- Build a reproducible benchmark record
- See Qwen3.5-122B capacity tiers
- See Qwen3.5-397B capacity tiers
- Compare large-model hardware paths
- Follow the high-VRAM watchlist
Primary sources
- NVIDIA H100 Tensor Core GPU specifications
- NVIDIA HGX H100/H200/B200 enterprise-reference components
- NVIDIA MIG supported-GPU table
Capacity calculations use decimal GB and compare artifact files with nominal device capacity. They are not runtime-memory guarantees. Product specifications and software support can change; confirm the exact system before purchase.
See the intermediate and upper tiers: compare H100, H200, B200 and B300 in one sourced decision hub.