The H200 is a memory upgrade on the Hopper architecture, not a new compute platform. This article covers why bandwidth matters as much as VRAM capacity, where the 141GB floor changes what fits on a single GPU, and how the NVL PCIe variant differs from the SXM5 for dedicated private infrastructure.
Category: v5 hardware
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.
OpenMetal GPU clusters: dedicated single-tenant multi-GPU infrastructure. All-RP6000, all-H200, or mixed on a private 40 Gbps mesh, fixed monthly pricing.
OpenMetal NVIDIA RTX Pro 6000 GPU server: 96GB GDDR7, FP4, dual Xeon 6530P, 1TB DDR5. Training and inference, single-tenant, fixed monthly pricing.
Q: What is the difference between the NVIDIA RTX Pro 6000 and H100? The RTX Pro 6000 is a Blackwell GPU with 96GB of GDDR7 and native FP4, while the
Q: Is the RTX Pro 6000 better than the L40S for AI inference and training? For most training and inference the RTX Pro 6000 outperforms the L40S on a single
Add NVIDIA RTX Pro 6000 or H200 GPU servers to an existing OpenMetal cloud or bare metal deployment – same private network, fixed monthly pricing.
NVIDIA RTX Pro 6000 vs H100: specs, cost, deployment fit. 96GB GDDR7 + FP4 vs 80–94GB HBM3. OpenMetal offers the RP6000 and H200 on bare metal.
NVIDIA RTX Pro 6000 vs L40S for AI training and inference: 96GB GDDR7 + FP4 (Blackwell) vs 48GB GDDR6 (Ada). OpenMetal runs the RP6000 on bare metal.
Q: Can I attach RP6000 GPU nodes to an existing OpenMetal bare metal or Hosted Private Cloud deployment? Yes, you can attach RP6000 GPU nodes to an existing OpenMetal Hosted
Q: What is the difference between the NVIDIA RTX Pro 6000 and L40S? The RTX Pro 6000 is a newer Blackwell-generation GPU with 96GB of GDDR7 and native FP4, while
Q: Can I build a mixed GPU cluster with RP6000 and H200 servers? Yes, OpenMetal builds mixed GPU clusters that combine RP6000 and H200 nodes on the same private network,
Q: What is FP4 (NVFP4) and why does it matter for AI workloads? FP4 (NVFP4) is a Blackwell-native 4-bit floating-point format that increases low-precision inference throughput beyond the FP8 ceiling
Q: How does OpenMetal’s fixed-cost GPU pricing avoid the cloud “idle silicon tax”? OpenMetal charges a fixed monthly rate for a dedicated GPU server, so running the card at 100%
Q: Can I train and fine-tune AI models on the OpenMetal RP6000, or is it only for inference? Yes, the OpenMetal RP6000 trains and fine-tunes AI models as well as
Q: How much GPU memory does the OpenMetal RP6000 have? Each OpenMetal RP6000 GPU carries 96GB of GDDR7 memory, and a server can hold one or two cards for up
Q: GDDR7 vs HBM3: which matters for AI training and inference? GDDR7 offers high capacity at lower cost, while HBM3/HBM3e delivers much higher memory bandwidth; bandwidth is what matters most
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 40 Gbps mesh, built to order for
Q: Can I add GPU servers to my existing OpenMetal cloud or bare metal deployment? Yes, you can add NVIDIA RTX Pro 6000 or H200 GPU servers to an existing
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 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
Q: How does OpenMetal GPU pricing compare to AWS GPU instances? OpenMetal prices GPU servers on a fixed monthly model with included egress, while AWS bills GPU instances per GPU-hour
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
Learn how to enable Intel SGX and TDX on OpenMetal’s v4 and v5 servers. This guide covers required memory configurations (full channel allotment and 1TB RAM), hardware prerequisites, and a detailed cost comparison for provisioning SGX/TDX-ready infrastructure.
The v5 generation can be told as a cores-and-clocks story, but a significant change is bandwidth: the private fabric doubled to 40 Gbps, memory moved to DDR5-6400, and the lane budget grew to 88 PCIe 5.0 lanes.
Running AI inference on sensitive data requires hardware-level isolation, not just software controls. This guide covers how to build a confidential inference pipeline on OpenMetal’s XL v5 using Intel TDX, including Trust Domain setup, vLLM deployment, attestation, and storage architecture.
All-NVMe OSDs, an isolated boot pool, a clean lane budget, and identical nodes: how OpenMetal’s v5 hardware makes Ceph behave predictably instead of needing tuning.
The OpenMetal XL v5 is built on dual Intel Xeon 6530P processors (Granite Rapids, Intel 3 process) with 1TB DDR5-6400, 25.6TB of Micron 7500 MAX NVMe, and full Intel TDX support as a base configuration. This article covers the workloads it’s built for, why TDX matters for specific use cases, how the private cloud and bare metal configurations compare, and where it fits in the v5 lineup relative to the Large.
The OpenMetal Large v5 is built on Intel’s Granite Rapids architecture with 92% more L3 cache, a 14% higher base clock, and double the RAM and NVMe of the Medium v5. This guide covers the workloads it handles best, how the private cloud and bare metal configurations compare, and where it fits alongside the Medium and XL v5.
Sometimes you want a cloud, not a server, but on terms you control. A guide to the hosted private cloud workloads that fit OpenMetal v5: VMware migration, multi-team internal IaaS, SaaS platforms, dev and test fleets, Kubernetes on OpenStack, and S3-compatible object storage on Ceph.
Not every workload belongs on a shared cloud instance. A guide to the bare metal workloads that run best on OpenMetal v5, from databases and virtualization to Kubernetes, CPU-based AI inference, analytics, and confidential computing, and why dedicated Xeon 6 hardware makes the difference.