How to choose between HGX B300, B200, H200 and H100

Start with the workload, not the GPU

The right system depends on what you will run: pre-training a model from scratch, fine-tuning an existing model, or serving inference to users. Each puts different pressure on GPU memory, interconnect and networking.

Before comparing systems, write down your largest model size, expected number of users or requests, data volumes and when you need to be in production.

GPU memory decides what fits

Memory per GPU sets how large a model you can train or serve without splitting it across many servers. More memory usually means fewer servers for the same model, and simpler operations.

• HGX B300 – 288 GB HBM3e per GPU, 2.3 TB per server
• HGX B200 – 180 GB HBM3e per GPU, 1.4 TB per server
• HGX H200 – 141 GB HBM3e per GPU, 1.1 TB per server
• HGX H100 – 80 GB HBM3 per GPU, 640 GB per server

When each system makes sense

• B300: frontier-scale training and reasoning inference, where memory and throughput matter most.
• B200: large-model training and high-volume inference with strong price-performance.
• H200: memory-heavy inference and teams that want a mature, widely supported platform.
• H100: established training and inference with the widest software maturity, and often the easiest starting point.

Don't forget the site

Blackwell systems draw more power per server than Hopper, and some configurations are liquid-cooled. Check rack power, cooling and floor loading before choosing – it can change the answer.

Plan the network with the servers

Multi-node training needs a fast fabric – InfiniBand or 400/800G Ethernet – so GPUs aren't left waiting. Budget for switches, NICs, optics and storage at the same time as the servers.