AMD has proven viability with Meta/OpenAI wins—but software ecosystem gap and supply constraints limit near-term ceiling.
MI300X — Current Flagship
- HBM3 Memory: 192GB (vs 80GB H100)
- Memory Bandwidth: 5.3 TB/s
- Cost/H100e: $12,500
Key Metrics
- Compute Share: 5.8%
- Revenue Share: 3.2%
- Revenue: $9.8B
Roadmap: MI350X (2025)
- Performance Target: 35x inference vs MI300X
- Architecture: CDNA 4, 3nm process
- Memory: HBM3E
Major Customer Wins
- Meta: Llama 4 training 100% AMD, MI300X clusters
- OpenAI: Infrastructure deal, 6 GW campus partnership
- Microsoft: Azure instances, MI300X VMs, cloud availability
Why AMD Is Winning Deals
- Memory Advantage: 192GB HBM3 vs 80GB — fits larger models
- NVIDIA Alternative: Customers want supply chain diversity
- Price/Performance: ~20% cheaper than NVIDIA B300 equivalent
ROCm Software Stack
- Current Status: PyTorch support improving rapidly
- Gap vs CUDA: Smaller library set, 17 years behind
The CUDA Challenge
- CUDA Head Start: 17 years (2007)
- Developer Base: 4M+ vs ~500K
- AMD Strategy: PyTorch-first focus
Supply Chain Challenge
- CoWoS capacity: NVIDIA controls 70%+
- AMD getting: ~30% of remaining capacity
2025 Data Center GPU Revenue Target
$12B+ (up from $6.8B in 2024)
Strategic Position: Credible Challenger. AMD has proven viability with Meta/OpenAI wins—but software ecosystem gap and supply constraints limit near-term ceiling.
This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.
