NVIDIA HGX B300 vs B200

Both are 8-GPU NVIDIA HGX nodes, one Blackwell tier apart. The HGX B300 is built on Blackwell Ultra; the HGX B200 is the original Blackwell node.

The defining differences are memory and scale-out. Each B300 GPU carries 288 GB of HBM3e — 2,304 GB across the node — versus 180 GB (1,440 GB per node) on the B200, and the B300 pairs with ConnectX-8 SuperNICs at 800 Gb/s per GPU against ConnectX-7 at 400 Gb/s on the B200. What is common: the same fifth-generation NVLink at 1.8 TB/s per GPU and the same 8 GPUs per node. In short, the B300 is a memory-and-fabric step-up on a shared platform — reach for it when a bigger model or more headroom justifies it, while the B200 remains available now and strong for most training and inference.

Per-GPU and per-node figures for the two 8-GPU Blackwell platforms. The headline moves are memory — 288 GB vs 180 GB per GPU (2,304 GB vs 1,440 GB per node) — and scale-out — ConnectX-8 at 800 Gb/s vs ConnectX-7 at 400 Gb/s. NVLink generation and GPU count are the same across both.

Blackwell Ultra raises on-package HBM3e from 180 GB to 288 GB per GPU — a 60% step — so an 8-GPU B300 node holds 2,304 GB of HBM3e against 1,440 GB on the B200. More capacity means a larger model, and its long-context KV cache, stays resident on the same 8 GPUs with less spillover across the node.

The second change is the scale-out plane. The B300 pairs with ConnectX-8 SuperNICs at 800 Gb/s per GPU; the B200 uses ConnectX-7 adapters at up to 400 Gb/s with GPUDirect. When you cluster many nodes, that doubling of per-GPU scale-out bandwidth keeps gradients and activations moving faster between nodes. The fifth-generation NVLink baseboard (1.8 TB/s per GPU) that ties the 8 GPUs together inside the node is the same on both.

Compute is a wash at FP8 — both HGX nodes deliver ~4.5 PFLOPS of dense FP8 per GPU — but Blackwell Ultra raises dense FP4 from ~9 to ~13.5 PFLOPS per GPU (per NVIDIA’s DGX B300 datasheet — 108 PFLOPS dense FP4 per 8-GPU node — the 1.5× NVFP4 step over B200), the throughput lever for high-volume inference. These are dense HGX-node ratings; the sparsity numbers run 2× higher, and the rack-scale GB200/GB300 superchips run a higher clock bin (~5 / ~10 and ~5 / ~15 PFLOPS dense per GPU).

Step up to the HGX B300 when memory or fabric is the ceiling:

• Bigger models and more headroom. 288 GB per GPU (2,304 GB per node) keeps larger models and longer contexts resident, which matters most for frontier-scale training and the highest-capacity inference.
• Multi-node clusters. ConnectX-8 at 800 Gb/s per GPU doubles scale-out bandwidth over the B200’s ConnectX-7, feeding large all-reduce traffic when you connect many nodes.
• Multi-year runway. If you are standing up new capacity meant to stay current, Blackwell Ultra is the longer horizon and the higher-memory option.

The HGX B200 remains a strong, current Blackwell node — often the smarter buy:

• Available now, strong across the board. 180 GB of HBM3e per GPU (1,440 GB per node) and ~64 TB/s of aggregate node bandwidth handle large-model training and inference comfortably.
• Value and fit. Where 180 GB per GPU is enough and you are not saturating scale-out, the B200 delivers most of the useful throughput at a lower total cost.
• Integrator choice. The B200 ships factory-integrated from Lenovo, Supermicro, and GIGABYTE, in liquid- and air-cooled builds that fit a range of facilities.

Both platforms are new, factory-integrated, and available now — the HGX B300 from integrators including Supermicro, HPE, and Dell, and the HGX B200 from Lenovo, 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 GPU catalog to compare integrations.