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
Tag: h200
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.