Choosing between the NVIDIA H200 and B200 SXM is one of the most consequential infrastructure decisions an AI team can make in 2026. Both are exceptional, but they serve different workload profiles.
H200 SXM: the proven workhorse
The H200 delivers 141GB HBM3e memory with 3.35TB/s bandwidth — a significant step up from the H100. For LLM training runs that fit within that memory envelope, H200 clusters offer excellent price-to-performance and are widely available through wholesale providers today.
B200 SXM: built for scale
The B200 brings 192GB HBM3e per GPU and NVLink 5 with 1.8TB/s chip-to-chip bandwidth. For frontier model training and large-scale inference serving, the B200 is meaningfully faster — but supply remains tighter and lead times are extending into Q3.
Which should you choose?
If your workload fits in H200 memory and you need capacity within the next 2–4 weeks, H200 clusters are the pragmatic choice. If you are planning a 6-month training run starting in Q3, securing B200 capacity now through GPUaaS.com is worth the lead time.