The Artificial Intelligence revolution runs on silicon, and for the last two years, one chip has ruled the world: the H100. But the king has been dethroned. With the announcement of the Blackwell platform, everyone in the tech industry is now analyzing the NVIDIA H100 vs B200 specs to understand the future of AI training.
If you are an investor, a data scientist, or a tech enthusiast, understanding the leap between these two generations is critical. The gap in the NVIDIA H100 vs B200 specs isn’t just an incremental update; it is a fundamental shift in how computers “think.”
In this guide, we break down the technical architecture, memory bandwidth, and performance benchmarks. We will compare the NVIDIA H100 vs B200 specs to see if the “Blackwell” architecture truly lives up to the hype of being the engine of the new industrial revolution.
The Architecture Shift: Hopper vs. Blackwell
To truly understand the NVIDIA H100 vs B200 specs, we must look at the architecture. The H100 was built on the “Hopper” architecture, named after Grace Hopper. It was a marvel of engineering, packing 80 billion transistors.
The B200, built on the “Blackwell” architecture, takes a different approach. When looking at the NVIDIA H100 vs B200 specs, the most obvious difference is the sheer size. The B200 is actually two silicon dies connected by a 10TB/s interface, behaving as a single chip.
- H100 (Hopper): 80 Billion Transistors.
- B200 (Blackwell): 208 Billion Transistors.
This massive increase in transistor count is the primary driver behind the performance gap in the NVIDIA H100 vs B200 specs.
Memory and Bandwidth: The Bottleneck Breaker
In the world of Large Language Models (LLMs), compute power is important, but memory speed is king. If the chip cannot feed data fast enough, the processor sits idle. This is where the NVIDIA H100 vs B200 specs show the biggest divergence.
The H100 utilizes HBM3 (High Bandwidth Memory). The B200 upgrades this to HBM3e.
NVIDIA H100 Memory Specs
- Memory Capacity: 80GB (SXM5 version).
- Memory Bandwidth: 3.35 TB/s.
NVIDIA B200 Memory Specs
- Memory Capacity: 192GB.
- Memory Bandwidth: 8 TB/s.
When comparing the NVIDIA H100 vs B200 specs, the B200 offers nearly 2.5x the memory capacity and bandwidth. This means larger models (like GPT-4 or Llama 3 70B) can fit onto fewer chips, drastically reducing the cost of running AI.
Performance Benchmarks: Inference and Training
Raw numbers are nice, but how do the NVIDIA H100 vs B200 specs translate to real-world speed?
NVIDIA CEO Jensen Huang claimed that the B200 offers a “30x performance increase” in inference for massive models compared to the H100.
1. Training Speed
When training a 1.8 Trillion parameter model (roughly the size of GPT-4):
- H100: Requires 8,000 GPUs for 90 days and consumes 15 Megawatts of power.
- B200: Requires 2,000 GPUs for 90 days and consumes only 4 Megawatts.
2. Inference (Running the Model)
This is where the NVIDIA H100 vs B200 specs matter for end-users. The new “Second-Generation Transformer Engine” in the B200 allows for 4-bit floating point (FP4) precision.
- The Result: The B200 can generate tokens (text) significantly faster and cheaper than the H100.
Comparison Table: NVIDIA H100 vs B200 Specs
Here is the side-by-side breakdown of the NVIDIA H100 vs B200 specs for quick reference.
| Feature | NVIDIA H100 (Hopper) | NVIDIA B200 (Blackwell) |
|---|---|---|
| Architecture | Hopper | Blackwell |
| Transistors | 80 Billion | 208 Billion |
| Memory | 80GB HBM3 | 192GB HBM3e |
| Bandwidth | 3.35 TB/s | 8 TB/s |
| FP8 Performance | 4,000 TeraFLOPS | 20,000 TeraFLOPS |
| Interconnect | 900 GB/s NVLink | 1.8 TB/s NVLink |
| Power Consumption | ~700W | ~1000W+ (configurable) |
The table clearly shows that the NVIDIA H100 vs B200 specs are not in the same league. The B200 is effectively a generational leap designed specifically for the trillion-parameter era.
Pricing and Availability
Discussions about NVIDIA H100 vs B200 specs are incomplete without mentioning cost.
- The H100 currently sells for between $25,000 and $40,000 per chip on the secondary market.
- While official B200 pricing is fluid, analysts estimate the B200 will cost between $30,000 and $40,000, but with much higher value per dollar due to the performance density.
For enterprise buyers, the “Total Cost of Ownership” (TCO) makes the B200 superior, despite the high upfront tag, because you need fewer chips to do the same work.
Conclusion: Is the Upgrade Worth It?
When we analyze the NVIDIA H100 vs B200 specs, the conclusion is clear. The H100 remains an incredibly powerful chip that will power AI for years to come. However, the B200 is the future.
For massive data centers and companies training frontier models (like OpenAI, Google, and Meta), the difference in NVIDIA H100 vs B200 specs regarding energy efficiency and memory bandwidth makes upgrading a necessity, not a luxury.
As the supply chain stabilizes in late 2025 and 2026, we expect the B200 to become the new gold standard for Artificial Intelligence hardware.
