NVIDIA Hopper vs Blackwell
H200 vs B200: Understanding NVIDIA's Next-Generation AI Architectures
A comprehensive technical comparison of NVIDIA's Hopper and Blackwell GPU architectures, covering performance, specifications, and real-world applications for AI training and inference.
More Transistors
Faster FP8 Performance
More HBM Capacity
TL;DR: What's the Difference?
Hopper (H100/H200)
- ✓ Proven workhorse for LLM training (GPT-4, Llama, etc.)
- ✓ Excellent FP64/FP32 for scientific computing
- ✓ Up to 141GB HBM3e memory (H200)
- ✓ Lower power consumption (~700W)
- ✓ Widely available and well-supported
Blackwell (B100/B200)
- ⚡ 2.5x faster AI inference with FP4 precision
- ⚡ 2-die GPU design with 208B transistors
- ⚡ Up to 192GB HBM3e memory
- ⚡ 2x faster NVLink (1,800 GB/s)
- ⚡ Native FP4 support for ultra-efficient inference
Architecture Specifications
| Feature | Hopper | Blackwell | Improvement |
|---|---|---|---|
| Manufacturing Process | TSMC 4N | TSMC 4NP | Enhanced 4nm |
| Transistor Count | 80B | 208B | +160% |
| Dies per GPU | 1 | 2 | Multi-die |
| Max HBM Capacity | 141 GB | 192 GB | +36% |
| HBM Bandwidth | 4.8K GB/s | 8.0K GB/s | +67% |
| NVLink Bandwidth | 900 GB/s | 1800 GB/s | +100% |
| Max Power (TGP) | 700W | 1200W | +71% |
AI Performance Comparison
| Precision | Hopper Peak | Blackwell Peak | Speedup | Notes |
|---|---|---|---|---|
| FP8 | 2.0K TFLOPS | 5.0K TFLOPS | 150% faster | Standard AI training/inference |
| FP4 (NVFP4) | — | 10.0K TFLOPS | NEW | Ultra-efficient inference (2.5x vs Hopper FP8) |
Which Should You Choose?
Choose Hopper (H100/H200) if:
- ✓ LLM Training: You're training large language models and need proven stability
- ✓ Scientific Computing: Your workload requires FP64 precision
- ✓ Cost-Conscious: You want more GPUs for your budget
- ✓ Power Constraints: Your datacenter has power limitations
- ✓ Availability: You need hardware now (wider availability)
Choose Blackwell (B100/B200) if:
- ⚡ Inference at Scale: You're running LLM inference services (ChatGPT-style apps)
- ⚡ Largest Models: You're training trillion+ parameter models
- ⚡ Memory-Bound: Your workloads need 192GB+ per GPU
- ⚡ Multi-GPU Training: You're scaling across 100+ GPUs with NVLink
- ⚡ Cutting Edge: You want the absolute best performance and latest features
Example GPU Models
Hopper Architecture GPUs
Frequently Asked Questions
It depends on your use case. For inference workloads, Blackwell's FP4 support and 2.5x performance improvement can significantly reduce serving costs. For training, the benefit is more modest (~2x faster) but the larger memory capacity enables training bigger models. If power and cost aren't concerns, Blackwell is the clear choice.
Blackwell uses two GPU dies connected via a high-speed interconnect, effectively creating one massive GPU with 208 billion transistors. This allows NVIDIA to exceed the reticle limits of current chip manufacturing while maintaining high yields. The two dies communicate so quickly that software sees them as a single GPU.
NVIDIA announced Blackwell in March 2024, with initial availability expected in late 2024/early 2025. The B100 and B200 will likely be available in hyperscaler datacenters first, with broader availability in 2025. H200 (Hopper) remains the flagship for 2024 and is more readily available.
Hopper's H200 tops out at 700W, while Blackwell's B200 goes up to 1,200W - a 71% increase. However, performance-per-watt is actually better on Blackwell for AI workloads thanks to FP4 support. If you have power constraints, consider running more H200s instead of fewer B200s.
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