192 GB of VRAM: The MI300X's Killer Feature
The AMD Instinct MI300X carries 192 GB of HBM3 with 5.3 TB/s of memory bandwidth. Compare that to the H100's 80 GB and 3.35 TB/s. On raw hardware specs alone, the MI300X demolishes NVIDIA's flagship — 2.4x the VRAM and 1.58x the bandwidth. For memory-bound LLM inference, these numbers translate directly into real-world advantages.
A Llama-3 70B model in FP16 needs roughly 140 GB of VRAM. The MI300X fits it on a single GPU with 52 GB to spare for KV cache. The H100? Physically impossible — you need two GPUs, doubling your cost and adding inter-GPU communication latency. This single fact makes the MI300X compelling for anyone running large models.
Real Cloud Pricing
| GPU | Provider | On-Demand | Spot |
|---|---|---|---|
| MI300X 192GB | Crusoe | $3.45/hr | $0.95/hr |
| H100 80GB | Cudo Compute | $1.87/hr | — |
| H200 141GB | Vast.ai | $1.84/hr | — |
At $3.45/hr on-demand, the MI300X looks expensive next to the H100's $1.87/hr. But the spot price of $0.95/hr on Crusoe changes the calculation entirely — that's 49% cheaper than the cheapest H100 on-demand, for a GPU with 2.4x the VRAM.
The Software Problem: ROCm vs CUDA
Here's where the MI300X story gets complicated. AMD's ROCm software stack has improved dramatically, but it is not CUDA. In 2025, most AI frameworks officially support ROCm — PyTorch, JAX, and vLLM all work. But "works" and "works perfectly" are different things. You'll encounter edge cases: some custom CUDA kernels won't have ROCm equivalents, Flash Attention support can lag behind, and debugging tools are less mature.
What works well: Standard PyTorch training and inference, vLLM serving, Hugging Face Transformers, ONNX Runtime, and most mainstream LLM workflows. If your stack is relatively standard, ROCm handles it.
What's still rough: Custom CUDA kernels, some Flash Attention v2 edge cases, Triton kernel compilation (improving but slower), and niche frameworks that assume NVIDIA hardware. If you rely on cutting-edge custom kernels, budget extra engineering time.
The Availability Problem
The MI300X is currently only available from Crusoe in our dataset. That's a single provider versus 10+ providers offering H100s. Limited availability means less price competition, fewer region options, and a higher risk of stock-outs. This will improve as AMD pushes cloud partnerships, but today it's a real constraint.
The Verdict
The MI300X is the better hardware, full stop. 192 GB of VRAM and 5.3 TB/s bandwidth make it objectively superior for large-model inference. At $0.95/hr spot, the price-to-VRAM ratio is unbeatable. But software maturity and single-provider availability keep it from being the default recommendation. Use the MI300X if you run standard LLM workloads, want 70B+ single-GPU inference, and are comfortable with a less mature ecosystem. Stick with H100/H200 if you need battle-tested reliability, multi-provider options, or custom CUDA kernels.
