The NVIDIA H100 was the default Hopper-generation accelerator for AI training and inference. OpenMetal no longer carries it — the lineup is now the Blackwell RTX Pro 6000 for cost-efficient training and inference, and the H200 for the largest-memory, highest-bandwidth work. This page is a specs, cost, and deployment comparison of the RP6000 against the H100. For a head-to-head inference throughput study, see OpenMetal’s dedicated benchmark article: RTX Pro 6000 vs H100 for AI inference. For the largest training jobs, see the H200 spec page.
Key Takeaways
- Different memory technologies: the RP6000 has 96GB GDDR7; the H100 has 80GB (SXM) or 94GB (NVL) HBM3. The H100’s HBM3 delivers much higher memory bandwidth; the RP6000 offers comparable-or-greater capacity with newer architecture at lower cost.
- Newer generation + FP4: Blackwell (RP6000) adds native FP4 (NVFP4); the H100 (Hopper) tops out at FP8.
- Bandwidth vs cost: the H100’s HBM3 bandwidth favors bandwidth-bound large-scale training; the RP6000 favors cost-efficient training, fine-tuning, and high-throughput inference.
- Availability: OpenMetal offers the RP6000 today; the H100 is no longer carried. For HBM-class bandwidth, the H200 (141GB HBM3e) is OpenMetal’s current option.
- Same OpenMetal model: single-tenant bare metal, fixed monthly pricing, included egress — sustained training avoids the metered-cloud “idle silicon tax.”
Ready to Compare GPUs for Your Workload?
Tell us your model sizes, throughput targets, and whether you’re training or serving, and we’ll help you choose between the RP6000 and the larger-memory H200.
Spec Comparison
| Specification | NVIDIA RTX Pro 6000 Blackwell SE (OpenMetal) | NVIDIA H100 NVL | NVIDIA H100 SXM |
|---|---|---|---|
| Architecture | Blackwell | Hopper | Hopper |
| GPU Memory | 96GB GDDR7 | 94GB HBM3 | 80GB HBM3 |
| Memory Bandwidth | 1.79 TB/s | 3.9 TB/s | 3.35 TB/s |
| Lowest-Precision Tensor | FP4 (NVFP4) | FP8 | FP8 |
| Max Board Power | 600W | 350–400W | 700W |
| Carried by OpenMetal | Yes — available now | No (superseded) | No |
*The H100’s HBM3 gives substantially higher memory bandwidth; the RP6000 offers similar-or-greater capacity, a newer architecture with FP4, and lower cost.
Architecture and Precision: Blackwell vs Hopper
The RP6000 is Blackwell; the H100 is the prior Hopper generation. The functional differentiator is FP4 (NVFP4) — a Blackwell-native 4-bit format that increases low-precision inference throughput beyond the H100’s FP8 ceiling on supported stacks. For mixed-precision training, both are capable; the difference is less about tensor math and more about memory technology and cost (below). For the measured inference comparison on this GPU pairing, refer to the dedicated benchmark article rather than re-deriving it here.
Memory: GDDR7 Capacity vs HBM3 Bandwidth
This is the core engineering trade-off between the two cards:
- Capacity is comparable: 96GB (RP6000) vs 80–94GB (H100). The RP6000 holds as much or more model state per card.
- Bandwidth favors the H100: HBM3 (3.35–3.9 TB/s) significantly exceeds GDDR7 (1.79 TB/s). For bandwidth-bound large-scale training, that gap is real and matters.
The practical reading: if a workload is bandwidth-bound at large scale, HBM is the right memory — and OpenMetal’s current HBM option is the H200 (141GB HBM3e), not the discontinued H100. If a workload values capacity, FP4 throughput, and cost efficiency for training, fine-tuning, and serving, the RP6000 is the better economic fit.
Host Platform and Networking
On OpenMetal, the RP6000 runs on a single-tenant bare metal host with dual Intel Xeon 6530P (64C/128T), 1TB DDR5-6400, and a 6.4TB Micron 7500 MAX NVMe data drive, with 40 Gbps private and 10 Gbps public bandwidth — full root access, IPMI, and no hypervisor overhead.
Security and Confidential Computing
The RP6000 runs as a single-tenant bare metal device on OpenMetal — physical isolation, no shared hypervisor on the accelerator. Intel SGX is available on the host CPU. As on all OpenMetal GPU servers, Intel TDX and GPU passthrough cannot be combined in a single trust boundary. The RP6000 host is deployed in the HIPAA-compliant Ashburn (NTT DATA VA1) facility; OpenMetal offers BAAs at the organizational level.
When to Choose the RP6000 — and When You Want HBM
When the RP6000 is the right choice
- Cost-efficient training and fine-tuning where 96GB GDDR7 is sufficient
- High-throughput inference, especially where Blackwell FP4 helps
- Workloads that were sized for an H100 but don’t need HBM bandwidth
- Lower cost per card than HBM-class accelerators
When you want HBM bandwidth instead
- Bandwidth-bound large-scale training and the largest models
- In that case, OpenMetal’s current HBM option is the H200 (141GB HBM3e), the successor to the H100
Cost and Value
OpenMetal prices the RP6000 on a fixed monthly model with included egress — no per-GPU-hour metering. Because the H100 is no longer carried, the decision is not RP6000-vs-H100 on price but whether the RP6000 fits your workload versus stepping up to the HBM-class H200. For sustained, high-utilization training and always-on inference, the fixed-cost dedicated model avoids the metered-cloud “idle silicon tax” — where each GPU-hour bundles elasticity and idle-capacity premium into the rate, penalizing exactly the steady workloads GPUs are bought for. OpenMetal does not publish RP6000 pricing; contact OpenMetal for a custom quote.
Ready to Compare GPUs for Your Workload?
Tell us your model sizes, throughput targets, and whether you’re training or serving, and we’ll help you choose between the RP6000 and the larger-memory H200.
Deployment Options
- Dedicated GPU server — a single RP6000 (or dual-GPU) bare metal server with full root access and IPMI.
- Dedicated GPU cluster — multiple GPU nodes (all-RP6000 or mixed with H200) on a private 40 Gbps mesh.
- Attached to existing infrastructure — add RP6000 nodes to an existing OpenMetal Hosted Private Cloud or bare metal deployment.
Where to deploy
The RP6000 is available now in Ashburn, Virginia (US-East), with advance reservations available for Los Angeles, Amsterdam, and Singapore. Proof of Concept clusters are available for testing; ramp pricing is available for migrations.
Get an RP6000 Quote
Ready to deploy? Tell us about your AI/ML training or inference needs and we’ll provide a custom quote for the NVIDIA RTX Pro 6000 — as a single GPU server, a dedicated GPU cluster, or GPU nodes attached to an existing OpenMetal deployment.
- Single GPU server: One or two RP6000 cards with full root access and IPMI
- GPU cluster: Multi-node deployments (all-RP6000 or mixed with H200) on a private 40 Gbps mesh
- Attached GPU: Add RP6000 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
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.