AMD MI300X vs NVIDIA H100: The Definitive Cloud GPU Comparison

AMD's MI300X packs 192GB of HBM3 memory — more than double the H100's 80GB — and challenges NVIDIA's AI accelerator dominance. But does raw memory beat CUDA ecosystem maturity?

Specifications Head-to-Head

Where AMD MI300X Wins

• 192GB memory: Run Llama 3 405B on a single 8-GPU node
• 5.3 TB/s bandwidth: 58% more than H100, critical for memory-bound inference
• LLM inference throughput: 21% faster than H100 for 70B models
• Cost efficiency for large models: Fewer GPUs needed, offsetting higher per-card price

Where NVIDIA H100 Wins

• CUDA ecosystem: Years of optimization, wider community support
• Availability: H100 on 20+ cloud providers vs MI300X on ~5
• Training performance: Mature NVLink/NVSwitch interconnects for distributed training
• Software compatibility: Flash Attention, custom CUDA kernels all work natively

MI300X cloud options in 2026 include Oracle Cloud, Azure (limited regions), Vultr, and Fluidstack. H100 is available on Lambda Labs, CoreWeave, RunPod, Vast.ai, and many more.

Bottom line: If you're serving 70B+ models and comfortable with ROCm, MI300X offers better value. For everything else, H100 is the safer and often cheaper choice.