NVIDIA B200 Versus H200 Comparison Guide | Generated by AI
Question: B200 vs H200: Memory Performance and Key Differences
Answer: The transition from the Hopper (H200) to the Blackwell (B200) architecture represents a massive leap in memory bandwidth and raw compute power, specifically designed to handle trillion-parameter models.
Core Specifications Comparison
| Feature | NVIDIA H200 (Hopper) | NVIDIA B200 (Blackwell) | Improvement |
|---|---|---|---|
| Architecture | Hopper | Blackwell (Dual-Die) | Generational Leap |
| Memory Capacity | 141 GB HBM3e | 180 GB - 192 GB HBM3e | ~30% - 36% Increase |
| Memory Bandwidth | 4.8 TB/s | 8.0 TB/s | 1.67x Faster |
| NVLink Speed | 900 GB/s (v4) | 1.8 TB/s (v5) | 2x Faster |
| TDP (Power) | 700W | 1000W - 1200W | ~40% - 70% Higher |
| Transistors | 80 Billion | 208 Billion | 2.6x Density |
Memory Performance & Architecture
The B200 isn’t just a bigger chip; it’s a dual-die design connected by a high-speed interconnect (10 TB/s) that allows it to behave as a single unified GPU.
- Bandwidth Breakthrough: At 8.0 TB/s, the B200 is designed to prevent “memory starvation,” where the compute cores sit idle waiting for data. This is critical for Large Language Model (LLM) inference where memory bandwidth is often the primary bottleneck.
- HBM3e Utilization: Both use HBM3e, but the B200 features 8 stacks of HBM3e (24GB each) compared to the H200’s configuration. This allows for significantly larger KV (Key-Value) caches, which directly translates to supporting longer context windows in models like Llama 3 or GPT-4.
- Capacity for Trillion-Parameter Models: The 192GB capacity allows larger models to stay entirely within GPU memory, reducing the need for slower multi-GPU communication across the network.
Compute & Efficiency
- FP4 Support: The B200 introduces FP4 (4-bit Floating Point) precision. While the H200 is excellent at FP8, the B200’s FP4 capability allows it to deliver up to 15x the inference performance of the previous generation for certain workloads.
- The Power Trade-off: The B200 is a “beast” in terms of power, drawing up to 1,200W in some configurations. While it is more efficient per-token, it requires significantly more robust cooling infrastructure (often liquid cooling) compared to the H200’s 700W air-cooled friendly design.
Which one to choose?
- Choose H200 if: You have existing HGX/SXM infrastructure designed for 700W, or if your models fit comfortably within 141GB and you prioritize lower initial TCO and power consumption.
- Choose B200 if: You are training or serving frontier models (1T+ parameters), require the lowest possible latency for real-time applications, or need to maximize performance density in a new data center.
Would you like me to look into the specific performance benchmarks for a particular model, such as Llama 3 or DeepSeek, on these two chips?