NVIDIA HGX B200 vs H200
TL;DR
The HGX B200 is NVIDIA’s newer Blackwell platform: more memory (180 GB vs 141 GB per GPU), higher bandwidth, faster NVLink, and lower-precision FP4/FP8 tensor throughput built for frontier-scale training and the highest-throughput inference. The HGX H200 is the top of the Hopper generation — still excellent for memory-bound inference and large-model serving, and usually the better value and availability play. Both are new, 8-GPU nodes and available now; the right pick is workload-driven.
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The short answer
Both are 8-GPU NVIDIA HGX nodes, but a generation apart. The HGX B200 is built on the newer Blackwell architecture; the HGX H200 is the memory-upgraded top of the previous Hopper generation.
The B200 wins on raw capability — more HBM3e memory per GPU, higher memory bandwidth, a faster fifth-generation NVLink fabric, and Blackwell’s FP4/FP8 tensor throughput — which makes it the platform for frontier-scale training and the highest-throughput inference. The H200 is not obsolete: its 141 GB of HBM3e per GPU still makes it a strong choice for memory-bound inference and large-model serving, and it typically wins on value and availability. Both are new, factory-integrated, and available now.
Spec comparison
Per-GPU and per-node figures for the two 8-GPU HGX platforms. In short, the B200 carries ~28% more memory per GPU (180 GB vs 141 GB), roughly a third more bandwidth, and double the per-GPU NVLink bandwidth, on top of the added low-precision tensor formats. On compute, each B200 delivers roughly ~4.5 PFLOPS of dense FP8 and ~9 PFLOPS of dense FP4 per GPU against the H200’s ~2 PFLOPS of dense FP8 (1,979 TFLOPS, no FP4) — and the FP4 path is the lever for high-throughput inference. All compute figures are dense HGX-node ratings; NVIDIA’s "with sparsity" numbers are 2× higher.
| Spec | HGX B200 (Blackwell) | HGX H200 (Hopper) |
|---|---|---|
| GPU architecture | NVIDIA B200 (Blackwell) | NVIDIA H200 (Hopper) |
| HBM3e per GPU | 180 GB | 141 GB |
| HBM3e per 8-GPU node | 1,440 GB | 1,128 GB |
| Memory bandwidth | ~8 TB/s per GPU · ~64 TB/s aggregate per node | ~4.8 TB/s per GPU (~38 TB/s per node) |
| Peak FP8 (dense) | ~4.5 PFLOPS FP8 · ~9 PFLOPS FP4 per GPU | ~2 PFLOPS FP8 per GPU (1,979 TFLOPS · no FP4) |
| NVLink | 5th-gen NVLink · 1.8 TB/s per GPU | 4th-gen NVLink · 900 GB/s per GPU |
| Scale-out NIC | 400 Gb/s per adapter with GPUDirect (~3.2 Tb/s per node) | ConnectX-7 NDR400 InfiniBand · 400 Gb/s (~3.2 Tb/s per node) |
| Tensor precision | Blackwell 2nd-gen Transformer Engine · adds FP4/FP6, higher FP8 | Hopper Transformer Engine · FP8/FP16 |
| Typical use case | Frontier-scale training · highest-throughput inference | Memory-bound inference · value & availability |
When to choose the B200
Reach for the HGX B200 when the workload is compute- or fabric-bound and you want headroom for the next generation of models:
- Frontier and large-scale training. The higher memory, ~64 TB/s of aggregate node bandwidth, and 1.8 TB/s fifth-gen NVLink keep more of a large model resident and move gradients faster across the 8-GPU domain.
- Highest-throughput inference. Blackwell’s FP4/FP8 support lets you serve more tokens per second per GPU where the model and toolchain can use lower precision.
- Density and multi-year horizon. If you are standing up new capacity that must stay current for several years, the newer architecture is the longer runway.
When the H200 is still the right buy
The HGX H200 remains a strong, current choice — often the smarter buy:
- Memory-bound inference. Serving large models is frequently limited by memory capacity and bandwidth, not raw tensor math. 141 GB of HBM3e per GPU (1,128 GB per node) handles very large models and long-context KV caches comfortably.
- Value. For many production inference and fine-tuning workloads the H200 delivers most of the useful throughput at a lower total cost.
- Availability and facilities fit. Hopper nodes are broadly available, and air-cooled H200 configurations (e.g. the Dell PowerEdge XE9680) drop into standard facilities with no liquid loop — faster to deploy where a cooling retrofit is not on the table.
Procurement note
Both platforms are new, factory-integrated, and available now — the HGX B200 from integrators including Lenovo, Supermicro, and GIGABYTE, and the HGX H200 from Dell, Supermicro, and GIGABYTE. Configuration (cooling, CPU, networking) and pricing are quoted per configuration on request, since the figure depends on integrator, cooling, and scale-out fabric. Tell us the build you need and we will return pricing and availability. Browse the current GPU catalog to compare integrations.
Frequently asked questions
Is the B200 worth it over the H200?
It depends on the workload. For frontier-scale training and the highest-throughput inference — where more memory, bandwidth, faster NVLink, and FP4/FP8 all pay off — the B200 is worth the step up. For memory-bound inference and value-sensitive production serving, the H200 often delivers most of the useful throughput at a lower cost. Both are current, new, 8-GPU platforms.
How much more memory does the B200 have?
The HGX B200 carries 180 GB of HBM3e per GPU versus 141 GB on the HGX H200 — about 28% more. Across an 8-GPU node that is 1,440 GB versus 1,128 GB. The B200 also has higher memory bandwidth — roughly ~8 TB/s per GPU versus ~4.8 TB/s on the H200 (about ~64 TB/s vs ~38 TB/s across an 8-GPU node).
How much does an HGX B200 or H200 server cost?
GPU systems are quoted per configuration on request — the figure depends on model, integrator, cooling, and networking. Tell us the build you need and we will return pricing and availability.
Can the H200 still handle large-model inference?
Yes. With 141 GB of HBM3e per GPU (1,128 GB per 8-GPU node) and ~4.8 TB/s of per-GPU bandwidth, the H200 keeps very large models and their long-context KV caches resident, and remains one of the strongest platforms for memory-bound inference and large-model serving.
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