The Nvidia H100 vs H200 vs B200: AI chip evolution explained

The Evolution of NVIDIA's AI Chips

The H100 chip, sporting a new Hopper architecture, was a breakthrough by NVIDIA to strongly tailor GPU performance to deep learning models.

Soon after, NVIDIA launched the H200 chip, which had the same architecture as the H100 but with a bigger memory bank and higher memory bandwidth, making it faster for memory-bound workloads.

Built on the next-generation Blackwell architecture, the B200 emerges as the H100's successor, featuring a chiplet design, doubled memory capacity, next-level precision support, and massive bandwidth gains.

Key Points

- Debuted in 2022, the NVIDIA H100's Hopper Architecture was named after Grace Hopper, the famous American computing pioneer.

- The H200 maintains the same Hopper architecture as the H100 but with larger memory and higher bandwidth.

- B200 Tensor Core GPUs leverage a chiplet design on TSMC's 4NP node, double the memory capacity with HBM3e, support ultra-low precisions (FP4/FP6), and offer fifth-generation NVLink interconnect.

- While the corresponding leap in performance was not as drastic as the H100's jump over A100s, Blackwell chips still significantly outperform Hopper chips.

- Verified performance data of the B300 and B200 show up to 11–15× faster LLM throughput per GPU compared to the Hopper generation.

Understanding the Three Generations

The H100: Foundation for Modern AI

H100s are massive chips, with over 80B transistors. They are also incredibly specialized, with a dedicated transformer engine designed for language models up to 1 trillion parameters.

Hopper Architecture's tensor cores improved on predecessors by focusing on common calculation types needed for AI training. For instance, they could mix floating point precision types (FP8 and FP16 specifically), dramatically accelerating AI calculations.

The H100 established the baseline for modern AI infrastructure. H100s offer HMB3e (High Bandwidth Memory 3e) memory, developed by Micron; it's a memory powerhouse with a core voltage of 0.1 volts, exceptionally tall stack heights, and dense DRAM chips. Each pin on an HMB3e offers 6.4 Gbps.

The H200: Memory-First Optimization

The H200 maintains the same architecture as the H100 but with a bigger memory bank and higher memory bandwidth, making it faster for memory-bound workloads. This represents a focused engineering approach: rather than redesigning the core architecture, NVIDIA removed a critical bottleneck that limited performance on large language models.

H200s have 141 GB of HBM3e memory, compared to H100s' 80 GB.

B200s and B100s also have a higher memory bandwidth of 8 Tbps (compared to 4.8 for H200s and 3.2 for H100s). This extra memory capacity and bandwidth directly address the challenge of running increasingly large models without splitting them across multiple GPUs.

The B200: Architectural Redesign

With the H100, NVIDIA had nearly reached the reticle limit of semiconductor fabrication, which is the maximum die size that lithography machines can produce, leaving no headroom to simply scale up a single die further. The B200 solves this by packaging two unified "Blackwell GPU" dies on a single module, connected via the NV-High Bandwidth Interface (NV-HBI) at 10 TB/s. This allows the two dies to function as a single coherent GPU, effectively doubling the transistor budget without being constrained by die size limits.

Each B200 module packs 208 billion transistors (104 B per die), over 2.5× the transistor budget of H100.

Beyond FP8, the B200 adds support for FP6 and FP4, enabling up to 18 PFLOPS of sparse FP4 throughput for inference, surpassing the H100, which lacks support for these formats.

How It Works: The Technical Progression

1. Tensor Cores and Precision Formats

Each generation improved how GPUs handle the mathematical operations underlying AI. Hopper Architecture's tensor cores focused on common calculation types needed for AI training, mixing floating point precision types (FP8 and FP16 specifically), dramatically accelerating AI calculations. The B200 extends this with fifth-generation Tensor Cores that add support for FP4 and FP6 precision—formats that can double inference throughput for compatible workloads.

2. Memory Architecture Evolution

The progression shows how memory became the limiting factor. H100 offered sufficient memory for many workloads. H200 doubled down on memory capacity and bandwidth without changing the core compute design. B200 integrated dramatically more memory while also increasing bandwidth, addressing both constraints simultaneously.

3. Chiplet Design Innovation

Blackwell's dual-chip design connects two GPU dies with an ultra-high-bandwidth, low-latency interconnect that appears as a single device to software. This approach allows NVIDIA to deliver massive performance scaling while maintaining software compatibility and programmability.

Why It Matters

The evolution from H100 to H200 to B200 reflects how AI infrastructure must adapt to changing workload demands. Training large language models and running high-performance simulations now require GPUs with greater memory capacity, faster data movement, and highly efficient compute pathways. Enterprises are moving beyond smaller AI workloads toward models that span hundreds of billions of parameters. This shift has placed pressure on hardware to deliver higher throughput without increasing operational complexity or energy costs.

While the difference between H100s and H200s was due to improved memory and memory bandwidth, B100s and B200s have identical memory specs but different compute performance and power draw specs. This distinction matters: organizations can choose based on whether their bottleneck is memory access speed or raw computational throughput.

B200s have a higher power draw (1000W vs 700W), meaning they're more expensive to operate. This power requirement reflects the increased computational density packed into the chiplet design.

Related Terms

• Tensor Cores: Specialized processing units within NVIDIA GPUs designed to accelerate matrix multiplication operations fundamental to AI training and inference.
• HBM3e Memory: High Bandwidth Memory 3e, the latest generation of stacked DRAM used in data center GPUs, offering higher capacity and faster data transfer than previous generations.
• Chiplet Design: An architecture that combines multiple smaller processor dies on a single package, connected by high-speed interconnects, allowing greater total compute without hitting manufacturing limits.
• FP8/FP4 Precision: Lower-precision floating-point formats that reduce memory requirements and increase computational speed for inference workloads where maximum accuracy isn't required.
• NVLink: NVIDIA's high-bandwidth interconnect technology that enables multiple GPUs to communicate directly at very high speeds for distributed AI training.

Frequently Asked Questions

When should I choose H100 vs H200 vs B200?

If tokens per second at tight power is your KPI, choose B200. If KV-cache fits are the primary bottleneck, choose H200. If maturity and supply drive your schedule, choose H100. The H100 remains a mature, widely-available option. The H200 makes sense for organizations training or serving large language models that benefit from extra memory. The B200 is appropriate for teams pushing the boundaries of model scale or requiring maximum inference throughput.

What's the actual performance difference?

The H200 boosts inference speed by up to 2X compared to H100 GPUs when handling LLMs like Llama2. The B200 is playing in a different league entirely with that 15x improvement over H100 systems. However, these gains apply to specific workload types; not all AI tasks benefit equally from each generation.

Are H100s and H200s compatible with the same infrastructure?

Both H100 and H200 standard configurations operate at 700W maximum TDP, representing substantial thermal output that demands sophisticated cooling infrastructure. This means H200s can often be deployed as drop-in replacements for H100s in existing systems. The server platform of the B200 needs to be redesigned, which is not compatible with the H100. B200's higher power consumption and chiplet design require new infrastructure planning.

Last updated: May 21, 2026. For the latest energy news and analysis, visit stakeandpaper.com.