NVIDIA H200 vs H100 — GPU Comparison for AI Training and Inference

The NVIDIA H100 defined data center AI for the Hopper generation, but OpenMetal now carries its successor, the H200, instead. The two share the same Hopper compute architecture — the meaningful difference is memory: the H200 carries 141GB of HBM3e versus the H100’s 80–94GB of HBM3, with roughly 40% higher memory bandwidth. This comparison covers what that means in practice for training and inference, and why teams that were shopping for an H100 should look at the H200. See the H200 spec page for full details, and for a deeper inference-throughput study see OpenMetal’s RTX Pro 6000 vs H100 for AI inference.

Key Takeaways

• Same compute, more memory: the H200 uses the same Hopper GPU die as the H100, so per-GPU tensor throughput is comparable — the H200’s advantage is 141GB HBM3e vs 80–94GB and ~40% more memory bandwidth.
• Single-card model fit: a 70B model in 16-bit precision (~140GB) fits on one H200 but requires two H100s with tensor-parallel sharding — fewer GPUs, less inter-GPU latency, simpler deployment.
• Memory-bound inference wins: higher HBM3e bandwidth (4.8 TB/s vs 3.35–3.9 TB/s) directly raises tokens-per-second on memory-bound LLM inference and supports larger KV caches and longer context.
• Compute-bound parity: for workloads limited by raw FLOPS rather than memory, the generational gain is modest — both are Hopper-class.
• Availability: OpenMetal offers the H200 today (US-East / Ashburn); the H100 is no longer carried. Choosing the H200 is choosing the supported, available generation; NVIDIA AI Enterprise (NVAIE) is available for H200 deployments (contact OpenMetal for options).

Spec Comparison

*The H200 and H100 share the same Hopper compute die; compute-throughput rows are comparable by design. The differentiators are memory capacity, memory bandwidth, and availability.*

GPU Compute: Same Hopper Cores

Both the H200 and H100 are built on NVIDIA’s Hopper architecture and carry the same tensor and CUDA core configuration, so raw compute throughput — FP8, FP16/BF16, and FP64 — is comparable between them at a given form factor and power (both are the same Hopper compute die; peak tensor throughput tracks clock/power, so SXM parts run higher than NVL/PCIe). A workload that is compute-bound (limited by FLOPS, such as dense training on a model that already fits in memory) sees only a modest generational gain moving from H100 to H200. The H200’s value is not in faster math; it is in feeding that math from a larger, faster memory pool.

GPU Memory: The Real Difference

Memory is where the H200 separates from the H100:

• Capacity: 141GB vs 80–94GB. The H200’s HBM3e gives ~50% more capacity than the H100 NVL and ~76% more than the H100 SXM. This is the decisive factor for model fit — larger models, larger batches, and longer context windows live on a single card.
• Bandwidth: 4.8 TB/s vs 3.35–3.9 TB/s. Roughly 40% more memory bandwidth. Since LLM inference is overwhelmingly memory-bandwidth-bound, this translates fairly directly into higher tokens-per-second at the same precision.

Practical example: a 70B-parameter model at 16-bit precision needs ~140GB for weights alone. On the H200 that fits on one GPU; on the H100 it requires two cards with tensor-parallel sharding, adding inter-GPU communication overhead and deployment complexity. For inference serving, one H200 can replace two H100s for these model sizes.

Host Platform and Networking

On OpenMetal, the H200 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. The host platform is identical regardless of GPU generation — the comparison is purely about the accelerator. Full root access, IPMI, no hypervisor overhead, and included east-west traffic apply to every OpenMetal GPU server.

Security and Confidential Computing

Both GPUs run as single-tenant bare metal devices 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 H200 host is deployed in the HIPAA-compliant Ashburn (NTT DATA VA1) facility; OpenMetal offers BAAs at the organizational level for regulated AI workloads.

When the H200’s Extra Memory Matters — and When It Doesn’t

When the H200 is clearly the right choice

• Serving or fine-tuning 70B+ models where single-card fit eliminates multi-GPU sharding
• Memory-bandwidth-bound inference where tokens-per-second scales with HBM bandwidth
• Long-context inference and large KV caches
• Consolidating two-H100 deployments onto a single H200 per model replica

When the generational gain is smaller

• Compute-bound dense training on models that already fit in 80GB — both are Hopper-class on raw FLOPS
• Smaller models (≤13B) where neither card is memory-constrained — here a lower-cost inference GPU like the RTX Pro 6000 may be the better economic fit (see RTX Pro 6000 vs H200)

Cost and Value

OpenMetal prices the H200 on a fixed monthly model with included egress. NVIDIA AI Enterprise (NVAIE), NVIDIA’s supported runtime and NIM microservices, is available for H200 deployments; contact OpenMetal for current NVAIE options. Because the H100 is no longer carried, the practical decision is not H200-vs-H100 on price but whether the H200 is the right GPU for your workload versus a lower-cost inference card. For memory-bound and large-model work, the H200’s single-card fit often reduces total GPU count — fewer GPUs for the same job can lower total cost despite the larger per-card hardware. OpenMetal does not publish H200 pricing; contact OpenMetal for a custom quote.

Deployment Options

• Dedicated GPU server — a single H200 (or dual-GPU) bare metal server with full root access and IPMI.
• Dedicated GPU cluster — multiple GPU nodes (all-H200 or mixed) on a private 40 Gbps mesh for distributed training and scaled inference.
• Attached to existing infrastructure — add H200 nodes to an existing OpenMetal Hosted Private Cloud or bare metal deployment.

Where to deploy

The H200 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 from other providers.

Get an H200 Quote

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

• Single GPU server: One or two H200 cards with full root access and IPMI
• GPU cluster: Multi-node deployments on a private 40 Gbps mesh
• Attached GPU: Add H200 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; NVIDIA AI Enterprise (NVAIE) is available for H200 deployments (contact OpenMetal for current options).

Related OpenMetal Answers

• What is the difference between the NVIDIA H200 and H100?
• Is the NVIDIA H200 faster than the H100 for inference?
• Can a single H200 run a 70B parameter model?
• Why does OpenMetal offer the H200 instead of the H100?
• How much GPU memory does the H200 have compared to the H100?

Related Hardware

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