NVIDIA RTX Pro 6000 vs H200 — Which OpenMetal GPU Server Should You Choose?

OpenMetal offers two current-generation GPU servers, and they solve different problems. The RTX Pro 6000 (Blackwell, 96GB GDDR7) is the cost-efficient workhorse for training, fine-tuning, and high-throughput inference. The H200 (141GB HBM3e) is the larger-memory, higher-bandwidth option for the biggest models and bandwidth-bound work. Both run on the same single-tenant bare metal host with fixed monthly pricing. This page helps you pick — or decide to run both in a mixed cluster. See the full specs on the RP6000 and H200 pages.

Key Takeaways

• Memory capacity: H200 leads at 141GB HBM3e vs the RP6000’s 96GB GDDR7 — the H200 fits the largest models (e.g., 70B at 16-bit) on a single card.
• Memory bandwidth: H200’s HBM3e (4.8 TB/s) far exceeds the RP6000’s GDDR7 (1.79 TB/s) — decisive for bandwidth-bound training and inference.
• Precision: the RP6000 (Blackwell) adds native FP4 (NVFP4); the H200 (Hopper) tops at FP8 — the RP6000 can win on low-precision inference throughput.
• Cost efficiency: the RP6000 carries a lower per-card rate — the better economics for training, fine-tuning, and serving that fit in 96GB.
• Same platform, same economics: identical dual-Xeon-6530P host, single-tenant bare metal, fixed monthly pricing, included egress, and no per-GPU-hour “idle silicon tax” on either.
• Not either/or: mixed clusters can pair H200 nodes (big-model/bandwidth work) with RP6000 nodes (cost-efficient inference and training) on the same private mesh.

Spec Comparison

*Both train and infer; the split is memory capacity/bandwidth (H200) vs cost efficiency and FP4 (RP6000). 

How to Choose

Choose the RP6000 when

• Your models, training jobs, and batches fit comfortably in 96GB
• You want the best cost efficiency for training, fine-tuning, or serving
• Low-precision (FP4) inference throughput is valuable
• You’re running many cards and per-card cost dominates the budget

Choose the H200 when

• You need the largest single-card memory (e.g., 70B-class models in 16-bit without sharding)
• Your workload is memory-bandwidth-bound — large-scale training or high-throughput large-model inference where HBM3e’s 4.8 TB/s matters

Run both when

• A mixed fleet fits: H200 nodes for big-model training and bandwidth-bound serving, RP6000 nodes for cost-efficient inference and mid-scale training — on the same private 40 Gbps mesh. See OpenMetal GPU clusters.

Shared Platform and Economics

Both servers use the same single-tenant bare metal host — dual Intel Xeon 6530P (64C/128T), 1TB DDR5-6400, a 6.4TB Micron 7500 MAX NVMe data drive, 40 Gbps private / 10 Gbps public networking, full root access and IPMI. Both are priced on OpenMetal’s fixed monthly model with included egress, so sustained, high-utilization workloads avoid the metered-cloud “idle silicon tax.” Both are deployed in the HIPAA-compliant Ashburn (NTT DATA VA1) facility, with advance reservations available for other regions.

Deployment Options

• Dedicated GPU server — a single RP6000 or H200 (or dual-GPU) bare metal server.
• Dedicated GPU cluster — multiple nodes, all-RP6000, all-H200, or mixed, on a private 40 Gbps mesh.
• Attached to existing infrastructure — add either GPU to an existing OpenMetal Hosted Private Cloud or bare metal deployment.

Where to deploy

Both GPUs are available now in Ashburn, Virginia (US-East), with advance reservations for Los Angeles, Amsterdam, and Singapore. Proof of Concept clusters are available for testing; ramp pricing is available for migrations.

Get a GPU Quote

Ready to deploy? Tell us about your AI/ML workload and we’ll provide a custom quote for the RP6000, the H200, or a mixed GPU cluster — as single servers, a dedicated cluster, or GPU nodes attached to an existing OpenMetal deployment.

• Single GPU server: One or two RP6000 or H200 cards with full root access and IPMI
• GPU cluster: Multi-node, single- or mixed-GPU, on a private 40 Gbps mesh
• Attached GPU: Add GPU capacity to your existing Hosted Private Cloud or bare metal footprint

All deployments include fixed monthly pricing, included egress, a 99.96%+ network SLA, and DDoS protection.

Related OpenMetal Answers

• Should I choose the NVIDIA RTX Pro 6000 or the H200 for my workload?
• What is the difference between GDDR7 and HBM3e for AI?
• Can I mix RP6000 and H200 GPUs in one OpenMetal cluster?
• Which OpenMetal GPU is more cost-effective for AI training?
• Does the RP6000 or H200 have more GPU memory?

Related Hardware

• NVIDIA H200 vs H100 — GPU Comparison for AI Training and Inference
• Bare Metal GPU Server — NVIDIA H200 NVL — Dual Intel Xeon 6530P, 1TB DDR5, 141GB HBM3e
• OpenMetal GPU Clusters — Dedicated Multi-GPU Infrastructure for AI Training and Inference

Product specifications, pricing, and availability may change due to market conditions and other factors. For the most current information, please contact the OpenMetal team directly.