NVIDIA H200 and B200 GPU Architectures: Next-Gen HPC and AI Hardware

In late 2023, NVIDIA announced H200, a significant revision of the Hopper architecture: the same core design but upgraded to HBM3e memory, boosting capacity to 141 GB and bandwidth to 4.8 TB/s. In the second half of 2024, the next-generation Blackwell architecture (B200 and GB200) arrived: a dual-die design with FP4 support, 1,800 GB/s NVLink 5.0, and 8.0 TB/s memory bandwidth, promising 4–6× LLM training speed over the H100.

This article covers the technical architectures of H200 and B200, comparative performance data against H100, and guidance on which GPU fits which workload.

H200: Hopper Architecture, Refreshed with HBM3e

The H200 uses the same GH100 core architecture as the H100. The key change is the memory subsystem: HBM3e (Enhanced) memory replaces the H100’s HBM3 (3.35 TB/s, 80 GB).

H200 Specifications

The H200’s critical advantage is 76% more memory capacity and 43% higher memory bandwidth while using the same compute architecture:

• LLM inference: 141 GB fits a 70B parameter model on a single GPU without INT4 quantization. The same model requires 2 GPUs on an H100.
• Memory-bound HPC workloads: Larger problem sizes in GROMACS, ANSYS Fluent, and similar simulation applications can run on a single GPU.
• Gradient accumulation: Larger batch sizes mean more efficient training with the same GPU count.

The H200 uses the exact same software stack (CUDA 12.x, cuDNN, NCCL) as the H100. Upgrading from H100 to H200 requires only a GPU module swap — the same server platform (HGX H100) and cooling infrastructure are fully compatible.

B200: Blackwell Architecture — Dual-Die Design

Blackwell (B200) is NVIDIA’s next-generation GPU architecture introduced in 2024. Unlike Hopper, it houses two separate dies connected via NVLink in a single package. This design overcomes single-die limitations to deliver higher compute density and a larger memory footprint.

B200 Specifications

Blackwell Key Innovations

Second-Generation Transformer Engine: Adds FP4 (4-bit floating point) support. NVIDIA reports up to 2× higher throughput with comparable model accuracy compared to FP8 training, enabling much larger models under the same power budget.

NVLink 5.0: GPU-to-GPU bandwidth doubles to 1,800 GB/s, a 2× increase over H100’s NVLink 4.0 (900 GB/s). All-reduce operations in multi-node training see substantial speedups.

NVSwitch 5.0: Supports up to 576 GPUs at full NVLink bandwidth. DGX B200 (8 GPUs) and DGX SuperPOD B200 (288 GPUs) configurations can scale to 576 GPUs on a single fabric.

Confidential Computing: Blackwell introduces in-GPU memory encryption and trusted execution environment support. This is critical for defense, finance, and healthcare sectors handling sensitive data.

H100 vs H200 vs B200: Comprehensive Comparison

Workload-Based GPU Selection

When H200 Makes Sense

The H200 is the lowest-cost upgrade path for organizations with existing H100 infrastructure. Same server, same cooling, same software stack — only the GPU module changes.

• Large language model inference: 141 GB fits a 70B model on a single GPU without INT4 quantization.
• Memory-bound HPC: GROMACS, CP2K, OpenFOAM simulations can run larger grids on a single GPU.
• Protecting H100 investment: Organizations on the HGX H100 platform can upgrade to H200 without motherboard changes.

When B200 Makes Sense

The B200 is designed for organizations building new AI infrastructure or scaling existing capacity 5–10×.

• Large-scale LLM pre-training: 4–6× speedup over H100 for 70B+ parameter models.
• Ultra-efficient FP4 training: 2× larger models under the same power budget.
• Multi-tenant AI platforms: 14 MIG instances let a single B200 run 14 isolated AI workloads.
• Security-critical applications: Confidential Computing for defense and finance.

When H100 Still Makes Sense

As of 2026, the H100 still has the largest installed base and most mature software ecosystem:

• Budget-constrained projects: Lower per-unit cost than H200 or B200.
• Short-term (1–2 year) projects: Sufficient performance when ROI horizon is short.
• Expanding existing H100 fleets: Homogeneous hardware reduces operational complexity.

For a broader GPU selection framework, see our GPU Selection Guide.

Blackwell and NVIDIA’s Strategic Direction

Blackwell represents a significant inflection point in NVIDIA GPU design. The dual-die approach offers an alternative path to increasing compute density as Moore’s Law slows. FP4 support redefines the precision-performance tradeoff in AI training.

NVIDIA’s roadmap beyond Blackwell includes Vera Rubin (2027) and Feynman (2029). Rubin is expected to feature NVLink 6.0 (3,600 GB/s) and HBM4 memory.

GPU Infrastructure with Mevasis

Mevasis provides NVIDIA H100, H200, and B200 GPU infrastructure — deployment, management, and rental — for HPC and AI workloads. Contact our team for GPU configuration sizing, cost analysis, and infrastructure setup.

• GPU Cluster Solutions
• GPU Hourly Rental
• HPC Rental Portfolio

Frequently Asked Questions

Can the H200 be used in the same server as the H100? Yes. The H200 is fully compatible with the HGX H100 platform. It uses the same motherboard, cooling, and power infrastructure. Only the GPU module needs to be swapped.

Does the B200 require special cooling? The B200 has a ~1,000 W TDP. Direct liquid cooling is recommended. Air-cooled systems may require power capping.

Is FP4 training quality as good as FP8 or FP16? NVIDIA benchmarks show FP4-trained models reaching comparable accuracy to FP8-trained models. However, precision requirements vary by model and dataset; each workload should be evaluated individually.

Can I upgrade my existing H100 system to B200? No. The B200 requires a new platform (HGX B200). While H100 and H200 share platform compatibility, moving to B200 requires new server infrastructure.

Should I choose H200 or go directly to B200? For short-term (1–2 year) investments with existing H100 infrastructure, the H200 is the practical choice. For 3+ year greenfield investments, the B200 offers better long-term value.