---
type: "Comparison"
title: "Blackwell vs Hopper"
description: "NVIDIA Blackwell (B200) vs NVIDIA Hopper (H100)"
resource: "https://www.contextstudios.ai/comparisons/blackwell-vs-hopper"
language: "en"
tags: ["NVIDIA Blackwell vs Hopper", "B200 vs H100", "GPU comparison", "AI chip comparison", "NVIDIA GTC 2024"]
generated:
  by: "process:contextstudios-md/1"
  at: "2026-10-08T20:56:34.006Z"
status: "stable"
---

# Blackwell vs Hopper

## Detailed Comparison

| Factor | NVIDIA Blackwell (B200) | NVIDIA Hopper (H100) | Winner |
|--------|------|------|--------|
| Inference Performance | 30x higher inference throughput via FP4, 192GB HBM3e, and NVLink Switch. Holds 1T-parameter models entirely in VRAM. | Good inference performance in FP8 and FP16. Production deployment standard 2023-2025. Proven and broadly available. | NVIDIA Blackwell (B200) |
| Training Performance | 2-4x better than H100 for modern architectures via second-gen Transformer Engine. | Market standard for LLM training 2022-2025. Well-suited for models up to 70B parameters on single node. | NVIDIA Blackwell (B200) |
| Energy Efficiency | Significantly more efficient: FP4 enables 4x more operations per watt. | Good efficiency for its generation, but significantly less efficient than Blackwell. | NVIDIA Blackwell (B200) |
| Price and Availability | More expensive (B200: >$30k), limited availability in 2025. Long-term cheaper through efficiency gains. | Lower spot-market prices due to larger supply. H100: $25-28k, broad cloud availability. | NVIDIA Hopper (H100) |
| FP4 Support | Native FP4 at hardware level. Enables 2x more efficiency than FP8 without quality loss. | No native FP4. FP8 as lowest precision tier. INT4 possible but with latency overhead. | NVIDIA Blackwell (B200) |
| Memory Capacity | 192GB HBM3e per B200 chip. GB200 NVL72 = 1.4TB total. Enables 400B+ parameter models without model parallelism. | 80GB HBM3 per H100. DGX H100 = 640GB total. Sufficient for models up to ~70B without parallelism. | NVIDIA Blackwell (B200) |

## Key Statistics

- **NVIDIA B200 achieves 20 petaflops FP4 inference performance vs 4 petaflops FP8 on H100 - 5x improvement** — NVIDIA GTC (2024)
- **GB200 NVL72 can hold a 1-trillion-parameter model entirely in VRAM - first time possible without model parallelism** — NVIDIA GTC (2024)
- **H100 remains the dominant inference chip in cloud market with 60%+ market share at major cloud providers in 2025** — Cloudwatch (2025)
- **Forecast: Blackwell-based cloud instances will be 50% cheaper than equivalent H100 instances by end 2026** — Analyst forecasts (2025)