NVIDIA GPU Platform Comparison: H100, H200, GH200, GB200, and GB300 for Data Center Deployment

TL;DR

The choice between NVIDIA's current GPU platforms determines not just compute performance but also facility infrastructure requirements, cooling strategy, cabling complexity, and total deployment cost.

Choosing the Right NVIDIA GPU Platform

Every GPU data center deployment starts with a platform decision. The choice between NVIDIA's current GPU platforms — H100, H200, GH200, GB200 NVL72, and GB300 NVL72 — determines not just compute performance but also the facility infrastructure requirements, cooling strategy, cabling complexity, and total deployment cost.

This guide compares all five platforms from the perspective of physical deployment: what each platform demands from the data center, what it takes to install and commission, and how infrastructure requirements scale from the simplest to the most complex configurations.

Platform Specifications at a Glance

NVIDIA H100

The H100 is the foundational GPU of the Hopper architecture generation. Available in both SXM5 (for HGX baseboard configurations) and PCIe form factors, the H100 has been the volume GPU for AI training since its launch in 2022.

Key specifications for deployment planning: 80GB HBM3 memory per GPU, 700W TDP per GPU, air-cooled or liquid-cooled configurations available, standard 4U and 8U server form factors (DGX H100 is 8U), 8 GPUs per HGX baseboard with NVLink 4.0 providing 900GB/s GPU-to-GPU bandwidth. Maximum rack density for air-cooled H100 configurations is approximately 30-40kW per rack using standard hot aisle/cold aisle containment.

From a deployment standpoint, the H100 is the most forgiving platform. Air-cooled configurations use standard data center infrastructure with no special cooling requirements beyond adequate airflow capacity. The server form factors fit standard 19-inch racks, and the power requirements are manageable with conventional PDU configurations.

NVIDIA H200

The H200 is a memory upgrade to the H100, replacing HBM3 with HBM3e to deliver 141GB of memory per GPU — a 76% increase over the H100. The GPU compute architecture is identical to the H100. The H200 is designed as a drop-in replacement for H100 in existing HGX baseboards and server platforms.

Key specifications: 141GB HBM3e memory per GPU (4.8TB/s memory bandwidth), 700W TDP per GPU (identical to H100), same physical form factor and cooling requirements as H100, available in SXM5 and NVL (dual-GPU) configurations.

For deployment teams, the H200 changes nothing about the physical infrastructure. If a facility can deploy H100, it can deploy H200 in the same racks with the same power, cooling, and cabling. The upgrade path from H100 to H200 involves replacing GPU modules on existing HGX baseboards or swapping entire server nodes, with no changes to rack infrastructure.

NVIDIA GH200 Grace Hopper Superchip

The GH200 combines an NVIDIA Grace ARM-based CPU with a Hopper GPU on a single module connected by NVLink-C2C (Chip-to-Chip), providing 900GB/s of CPU-GPU bandwidth. This eliminates the PCIe bottleneck between CPU and GPU that exists in all x86-based GPU server configurations.

Key specifications: 96GB HBM3 or 141GB HBM3e GPU memory (depending on variant), 72 ARM Neoverse V2 CPU cores with up to 480GB LPDDR5X CPU memory, 1000W combined TDP (CPU + GPU), available in single-superchip and dual-superchip server configurations, liquid cooling recommended for dense configurations.

The GH200 introduces a different server architecture than traditional GPU servers. Instead of a dual-socket x86 server with multiple GPU accelerators, each GH200 module is a self-contained compute unit. Systems like the NVIDIA MGX reference design pack multiple GH200 modules into a single rack, creating a fundamentally different cabling and power topology than HGX-based systems.

Deployment complexity for GH200 is moderate — higher than H100/H200 due to the unique form factor and potential liquid cooling requirements, but lower than the rack-scale GB200/GB300 platforms.

NVIDIA GB200 NVL72

The GB200 NVL72 is the first Blackwell-generation rack-scale GPU system. It integrates 36 Grace CPUs and 72 Blackwell GPUs into a single rack connected by a fifth-generation NVLink domain providing 130TB/s of aggregate GPU-to-GPU bandwidth.

Key specifications: 192GB HBM3e per GPU (13.8TB total GPU memory per rack), approximately 120kW per rack, 100% liquid cooling required (direct-to-chip), 48U NVIDIA MGX rack form factor, 18 compute trays and 9 NVLink switch trays per rack, one exaflop of dense AI performance per rack.

The GB200 NVL72 represents a step change in deployment complexity. It is not a server-in-a-rack — it is a rack-scale computer that requires facility-level liquid cooling infrastructure, 120kW power delivery per cabinet position, and precise integration of hundreds of internal connections. The rack ships as a pre-assembled unit from the integrator (Dell, ASUS, Supermicro), but site preparation, cooling connection, network integration, and commissioning are substantial efforts.

NVIDIA GB300 NVL72

The GB300 NVL72 is the Blackwell Ultra upgrade to the GB200 NVL72. It uses the same rack-scale architecture but with upgraded GPUs that deliver 1.5x the compute performance and 1.5x the memory capacity of the GB200.

Key specifications: 288GB HBM3e per GPU (approximately 20TB total GPU memory per rack), 120kW+ per rack, 100% liquid cooling required, same 48U MGX rack form factor as GB200, fifth-generation NVLink with 130TB/s aggregate bandwidth, designed for trillion-parameter model training and test-time scaling inference.

From a deployment perspective, the GB300 NVL72 has identical infrastructure requirements to the GB200 NVL72. A facility built for GB200 can accept GB300 racks with no changes to power, cooling, or physical infrastructure. The upgrade path involves replacing compute trays within existing rack infrastructure, not replacing entire racks.

Leviathan Systems is currently deploying GB300 NVL72 infrastructure at hyperscale AI training facilities in Texas. The GB300 is the most demanding GPU platform ever deployed in production data centers, and our deployment guide covers the full process in detail.

Infrastructure Requirements Comparison

Power

| Platform | Per-GPU TDP | Per-Rack Power (typical) | Power Feed Type | |----------|-------------|--------------------------|-----------------| | H100 | 700W | 30-40kW (air) | Standard PDU | | H200 | 700W | 30-40kW (air) | Standard PDU | | GH200 | 1000W (module) | 40-60kW | Standard or busway | | GB200 NVL72 | ~1200W (module) | ~120kW | Busway required | | GB300 NVL72 | ~1200W (module) | ~120kW | Busway required |

The jump from H100/H200 to GB200/GB300 is a 3x increase in power per rack. This is not an incremental change — it requires fundamentally different power distribution architecture. Facilities designed for 10kW-per-rack enterprise workloads cannot be retrofitted for GB200/GB300 without major electrical infrastructure upgrades.

Cooling

| Platform | Cooling Type | Heat Rejection per Rack | Facility Requirement | |----------|-------------|------------------------|---------------------| | H100 | Air (standard) | 30-40kW | Hot aisle/cold aisle | | H200 | Air (standard) | 30-40kW | Hot aisle/cold aisle | | GH200 | Air or liquid | 40-60kW | RDHX or direct-to-chip | | GB200 NVL72 | Liquid only | ~120kW | CDU + chilled water | | GB300 NVL72 | Liquid only | ~120kW | CDU + chilled water |

Air-cooled H100/H200 deployments can be installed in any modern data center with adequate cooling capacity. GB200/GB300 deployments require dedicated liquid cooling infrastructure that many existing facilities do not have.

Network Complexity

| Platform | Ports per Server | NVLink Fabric | Inter-Rack Network | |----------|-----------------|---------------|-------------------| | H100 | 8-10 per 4U/8U | Intra-server only | InfiniBand or Ethernet | | H200 | 8-10 per 4U/8U | Intra-server only | InfiniBand or Ethernet | | GH200 | 2-4 per module | NVLink-C2C (CPU-GPU) | InfiniBand or Ethernet | | GB200 NVL72 | 18 compute trays + 9 NVLink switch trays | Rack-scale NVLink domain | InfiniBand or Ethernet | | GB300 NVL72 | 18 compute trays + 9 NVLink switch trays | Rack-scale NVLink domain | InfiniBand or Ethernet |

The cabling volume inside a GB200/GB300 rack is an order of magnitude greater than an H100 rack. The NVLink fabric alone consists of hundreds of connections between compute trays and switch trays, all of which must be verified for proper seating and full bandwidth.

Deployment Timeline

| Platform | Single Rack Assembly | Testing & Commissioning | Total per Rack | |----------|---------------------|------------------------|----------------| | H100 | 1-2 days | 1 day | 2-3 days | | H200 | 1-2 days | 1 day | 2-3 days | | GH200 | 2-3 days | 1-2 days | 3-5 days | | GB200 NVL72 | 2-3 days (pre-assembled) | 2-3 days | 4-6 days | | GB300 NVL72 | 2-3 days (pre-assembled) | 2-3 days | 4-6 days |

These timelines assume that all facility infrastructure (power, cooling, network) is already in place. Facility preparation can add weeks or months to the overall project timeline, particularly for liquid-cooled deployments in facilities that were not originally designed for high-density cooling.

Which Platform for Which Workload?

AI Training at Scale

For large-scale training of models with hundreds of billions or trillions of parameters, the GB200 NVL72 and GB300 NVL72 offer the highest performance per rack and the most efficient inter-GPU communication through the rack-scale NVLink domain. The GB300 is preferred for new deployments due to its 1.5x memory and compute advantage over the GB200.

AI Inference

For high-throughput inference serving, the H200 offers the best balance of memory capacity (141GB enables larger models to fit in a single GPU) and deployment simplicity (standard air-cooled racks). For inference workloads that require the lowest latency and highest throughput, the GB300 NVL72's enhanced attention performance provides significant advantages.

Mixed Workloads and Flexibility

For organizations running a mix of training and inference with evolving requirements, H200 in air-cooled configurations provides the most operational flexibility. Individual servers can be added, removed, or reconfigured without affecting other systems in the rack.

Cost-Sensitive Deployments

The H100 remains the most cost-effective GPU for many workloads, particularly inference and fine-tuning where the absolute highest performance is not required. The H100's mature ecosystem, wide availability, and compatibility with standard data center infrastructure make it the lowest-risk deployment choice.

Platform Selection Services

Leviathan Systems deploys all five current NVIDIA GPU platforms and can advise on platform selection based on your specific workload requirements, facility constraints, and budget. We provide site assessments that evaluate your existing infrastructure against the requirements of each platform and recommend the deployment approach that delivers the best return on your GPU investment.