GB200 vs B200: Chip, Superchip, and NVL72 Explained
The B200 is one Blackwell GPU, the GB200 is a Grace + 2-GPU superchip, and the NVL72 is a 72-GPU rack. What each is, the specs, and which to buy or rent.
TL;DR
- The B200 is a single Blackwell GPU. The GB200 is a superchip: one Grace CPU plus two Blackwell GPUs. The GB200 NVL72 is a liquid-cooled rack of 72 of those GPUs and 36 Grace CPUs acting as one NVLink domain. Three different products, same underlying silicon.
- NVIDIA's own figures: a GB200 superchip carries 372 GB of HBM3e and 16 TB/s of bandwidth; a GB200 NVL72 carries 13.4 TB and 576 TB/s across a single 72-GPU NVLink domain.
- The superchip/NVL72 form matters when a model is too big or too concurrency-hungry for one 8-GPU node. For most fine-tuning and mid-size inference, a plain HGX B200 node is the right, cheaper, air-coolable choice.
- Almost every page ranking for this query is run by a firm that rents or sells this exact hardware. Treat their framing accordingly.
The B200 is one Blackwell GPU. The GB200 is a superchip: one NVIDIA Grace CPU wired to two Blackwell GPUs over NVLink-C2C. The GB200 NVL72 is a rack that fuses 72 of those GPUs and 36 Grace CPUs into a single NVLink domain. Same silicon, three very different things to buy or rent.
Why "GB200 vs B200" is the wrong comparison
The search query assumes the B200 and GB200 are competing products you pick between. They aren't. They sit at different levels of the stack.
- B200 — the GPU itself. One Blackwell package (two reticle-limited dies fused to behave as one GPU). It ships on an HGX or DGX baseboard, typically eight to a node.
- GB200 — a Grace Blackwell superchip. NVIDIA's superchip page describes it as two Blackwell Tensor Core GPUs and one Grace CPU joined by the NVLink-C2C interconnect, giving the CPU and GPUs coherent access to each other's memory.
- GB200 NVL72 — a rack-scale machine. NVIDIA's NVL72 page describes a 72-GPU NVLink domain that acts as a single, massive GPU, built from compute trays holding 36 Grace CPUs and 72 Blackwell GPUs.
So "nvidia gb200 vs b200" really means: do I need the bare GPU, the CPU-attached superchip, or the whole rack? Worth noting who's answering that question for you on the results page — nearly every ranking page (Spheron, GMI Cloud, GetDeploying, Introl, WhiteFiber) is a GPU-rental or infrastructure vendor with a direct commercial stake in which answer you land on.
GB200 vs B200 vs GB200 NVL72: the spec comparison
All figures below are from NVIDIA's product pages unless marked. Per-GPU B200 numbers are derived by dividing NVIDIA's DGX B200 8-GPU totals, and are marked †.
| Dimension | B200 (single GPU) | GB200 (superchip) | GB200 NVL72 (rack) |
|---|---|---|---|
| What it is | One Blackwell GPU (2 dies) | 1 Grace CPU + 2 Blackwell GPUs | 36 Grace + 72 Blackwell GPUs |
| GPUs | 1 | 2 | 72 |
| CPU | None (x86 host in HGX node) | 1 Grace, 72 Neoverse V2 cores | 36 Grace, 2,592 cores |
| HBM3e capacity | ~180 GB † | 372 GB | 13.4 TB |
| HBM3e bandwidth | ~8 TB/s † | 16 TB/s | 576 TB/s |
| NVLink domain | Up to 8 GPUs (one node) | Scales via NVL36/NVL72 | 72 GPUs as one |
| NVLink | 5th-gen, 1.8 TB/s per GPU | 5th-gen + NVLink-C2C to CPU | 130 TB/s aggregate |
| CPU memory | Host DDR (n/a) | Up to 480 GB LPDDR5X | 17 TB LPDDR5X |
| Form factor | SXM module on HGX/DGX baseboard | Liquid-cooled superchip module | Liquid-cooled rack, 18 trays |
| Power | Not disclosed by NVIDIA | ~2,700 W per board ‡ | ~120 kW per rack ‡ |
| Best for | Broad, air-coolable workloads | Building block of NVL72 | Frontier training, dense inference |
‡ Power is not listed on NVIDIA's spec pages. SemiAnalysis reports roughly 2,700 W reaching each "Bianca" board (two B200s + one Grace) and about 120 kW per NVL72 rack.
A note on the memory numbers, because vendor blogs disagree: you'll often see "192 GB" quoted for a B200. NVIDIA's shipping DGX B200 lists 1,440 GB across eight GPUs — 180 GB each — while the GB200 superchip's two Blackwell GPUs total 372 GB, or 186 GB each. The GB200's GPUs are binned slightly higher. Reconcile to NVIDIA's own totals, not to a single headline figure.
When does the superchip or NVL72 form actually matter?
The B200 GPU inside all three is the same class of part. What you're really buying with the GB200 and NVL72 is domain size and CPU-GPU coherence, not raw per-GPU speed.
It matters when:
- A model won't fit, or shard cleanly, inside one 8-GPU node. An HGX B200 node is an 8-GPU NVLink domain. The NVL72 is a 72-GPU domain where any GPU reaches any other in a single NVSwitch hop. For trillion-parameter training or large mixture-of-experts models, that flat, all-to-all fabric keeps 72 GPUs working as one accelerator instead of as nine nodes stitched together over slower back-end networking — which SemiAnalysis notes is far slower than NVLink.
- Inference needs high concurrency at low latency. Serving a huge model to many simultaneous users benefits from the same large coherent domain and from Grace's LPDDR5X pool for KV-cache offload.
It does not matter — and you're overpaying — when your workload fits comfortably in one node. Fine-tuning, most retrieval-augmented serving, mid-size model inference, and batch jobs run fine on a plain HGX B200 node, which is air-coolable and cheaper to rent or rack. Qualitatively, superchip and NVL72 capacity commands a premium per GPU-hour across cloud rate cards; pay it only when the domain is the point.
What this means for you
If you're an engineer sizing a rental. Default to an HGX B200 node. Reach for GB200 NVL72 only when your model genuinely needs more than 8 GPUs in one coherent domain, or when profiling shows inter-node networking is your bottleneck. For single-GPU or workstation-class decisions, the trade-offs look very different — see our RTX PRO 6000 vs H100 comparison.
If you're a founder modeling cost. The NVL72 is a liquid-cooling, power, and capital commitment (about 120 kW per rack). Unless you're training frontier models or serving them at scale, that density is a liability, not an asset, and the economics of an older A100 or H100 node can still win for steady workloads.
If you're a technical operator planning a build. The dividing line is cooling and power. B200 HGX nodes can be air-cooled in existing facilities; GB200 superchips and NVL72 racks are liquid-cooled and assume a data center designed for them.
Where GB300 and B300 fit
These are not the newest Blackwell. NVIDIA has since shipped Blackwell Ultra — the B300 GPU and GB300 NVL72 rack. NVIDIA says the GB300 NVL72 lists 20 TB of GPU memory across 72 Blackwell Ultra GPUs. If you're buying today, confirm which generation a "Blackwell" quote refers to — GB200/B200 or GB300/B300. For the earlier Hopper-to-Blackwell jump, our H200 vs B200 comparison covers the generational step up.
Frequently Asked Questions
What is the difference between the GB200 and the B200?
The B200 is a single Blackwell GPU. The GB200 is a superchip that combines one Grace CPU with two Blackwell GPUs over NVLink-C2C, giving coherent CPU-GPU memory. The B200 is a component; the GB200 is a CPU-plus-GPU module built around two of them.
Is a GB200 just two B200s?
Almost, but not quite. A GB200 superchip contains two Blackwell GPUs plus a Grace CPU joined by NVLink-C2C. The CPU and its NVLink-C2C coherence are the difference — you get up to 480 GB of LPDDR5X and unified memory access, not just double the GPU count.
What is the GB200 NVL72?
The GB200 NVL72 is a liquid-cooled rack that links 36 Grace CPUs and 72 Blackwell GPUs into one NVLink domain of 13.4 TB HBM3e. NVIDIA describes it as acting like a single massive GPU, built for frontier-scale training and high-concurrency inference.
Is the GB200 faster than the B200?
Per GPU, no — it's the same Blackwell silicon. A GB200 is faster only because it packages two GPUs plus a CPU. The real speed advantage appears at NVL72 scale, where 72 GPUs in one NVLink domain outrun the same GPUs split across separate 8-GPU nodes.
Should you choose GB200 or B200 for AI workloads?
Choose a B200 HGX node for most workloads that fit in eight GPUs: fine-tuning, mid-size inference, batch jobs. It's cheaper and air-coolable. Choose GB200 NVL72 only when a model needs a coherent domain larger than one node, or for very high-concurrency serving.