Choosing Between the OpenMetal RP6000 and H200

Q: Should I choose the RP6000 or the H200 for my workload?

Choose the RP6000 for cost-efficient training, fine-tuning, and high-throughput inference that fit in 96GB, and the H200 when you need the largest single-card memory (141GB) or HBM-class bandwidth.

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Pick the RP6000 when your models and batches fit comfortably in 96GB of GDDR7, when Blackwell FP4 inference throughput is valuable, or when per-card cost dominates the budget across many cards. It is the workhorse for fine-tuning, small-to-mid-scale training, and production serving.

Pick the H200 when you need a 70B-class model in 16-bit precision on a single card without tensor-parallel sharding, when the workload is memory-bandwidth-bound (HBM3e delivers 4.8 TB/s vs the RP6000’s 1.79 TB/s).

You do not have to choose just one: a mixed cluster can pair H200 nodes for big-model and bandwidth-bound work with RP6000 nodes for cost-efficient inference and training, on the same private 40 Gbps mesh. Both run on the identical single-tenant bare metal host with fixed monthly pricing.