DeepSeek-R1 is a 671B MoE: its FP8 weights span about six discrete H200 cards. See the fit math, why MoE memory follows total params, and how to serve it.
Tag: h200
A practical guide to matching NVIDIA’s H100, H200, B200, and B300 to your model, your latency target, and your budget, starting from the workload instead of the spec sheet. Key
On latency-bound inference, the Model FLOPs Utilization your optimization stack can actually hold is capped by who else shares the box, not by the kernel that runs on it. Sustained
A first-party FP4 checkpoint moves GPU selection from memory capacity to native tensor-core format support, and inverts the usual verdict. Based on the Inkling-Small.
Prefill is compute-bound, decode is memory-bandwidth-bound. Why splitting inference into two purpose-fit GPU pools beats one uniform fleet.
Yes, Llama 3.3 70B runs on a single OpenMetal H200 at FP8 with full 128K context. See the VRAM fit math, KV-cache budget, and vLLM setup.
An ordered Day-2 playbook for a single-tenant H200: full root and IPMI, owning the CUDA stack, boot-data isolation, and a node-bounded blast radius.
NVIDIA H200 vs H100 for AI training and inference: 141GB HBM3e vs 80–94GB, same Hopper compute with more memory. OpenMetal runs the H200 on bare metal.
NVIDIA RTX Pro 6000 vs H200 on OpenMetal: 96GB GDDR7 + FP4 for cost-efficient AI vs 141GB HBM3e for the largest models. Both single-tenant bare metal.
OpenMetal NVIDIA H200 bare metal GPU server: 141GB HBM3e, dual Xeon 6530P, 1TB DDR5. Single-tenant bare metal, fixed monthly pricing.
Q: Can I build a mixed GPU cluster with NVIDIA RTX PRO 6000 and H200 servers? Yes, OpenMetal builds mixed GPU clusters that combine RTX PRO 6000 and H200 nodes
Q: Can I run a 70B parameter LLM on a single OpenMetal H200? Yes, a single OpenMetal H200 runs a 70B-parameter model in 16-bit precision, because its 141GB of HBM3e
Q: Can I build a multi-GPU cluster with OpenMetal H200 servers? Yes, OpenMetal builds dedicated multi-GPU clusters of H200 servers on a private mesh, built to order for distributed training
Q: What NVMe storage does the OpenMetal H200 GPU server use? The OpenMetal H200 GPU server uses a 6.4TB Micron 7500 MAX NVMe SSD for data, plus two 960GB NVMe
Q: What CPU is paired with the OpenMetal H200 GPU server? Each OpenMetal H200 GPU server pairs the GPU with two Intel Xeon 6530P processors (Granite Rapids), giving 64 cores
Q: Should I choose the NVIDIA RTX PRO 6000 or the H200 for my workload? Choose the RTX PRO 6000 for cost-efficient training, fine-tuning, and high-throughput inference that fit in
Q: What is the difference between the NVIDIA H200 and H100? The H200 and H100 share the same Hopper compute architecture; the H200’s advantage is memory, with 141GB of HBM3e
Q: Is the NVIDIA H200 faster than the H100 for AI inference? For memory-bound LLM inference, yes: the H200’s higher HBM3e bandwidth (4.8 TB/s vs 3.35-3.9 TB/s) directly raises tokens-per-second,
Q: Why does OpenMetal offer the NVIDIA H200 instead of the H100? OpenMetal carries the H200 rather than the H100 because the H200 is the H100’s direct successor: 50% more
Real-time AI applications require consistent sub-100ms performance that multi-tenant cloud GPU instances can’t deliver. Explore how dedicated bare-metal H100/H200 clusters eliminate noisy neighbor effects, provide predictable pricing, and deliver the performance consistency needed for production inference systems.