ROCm Platform Options and AMD GPU Choices | Generated by AI

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Question: Which platforms offer AMD GPU products so I can run ROCm?

Answer:

There are multiple options — from AMD’s own developer cloud, to major cloud providers, to your own local hardware. Here’s a comprehensive breakdown:

1. AMD Developer Cloud (Official / Free Tier Available)

AMD has launched the AMD Developer Cloud, a fully managed, zero-setup environment providing instant access to AMD Instinct MI300X GPUs. With a GitHub account or email, developers can launch Jupyter notebooks in seconds and start building.

This is the easiest and most direct way to get started with ROCm.

2. Major Cloud Providers (Hyperscalers)

Microsoft Azure

Azure offers the ND MI300X v5 virtual machine powered by AMD Instinct MI300X GPUs, and ROCm integrates seamlessly with Azure for powerful AI and HPC workloads. The AMD Instinct MI300X GPU on this VM can run today’s gigantic frontier models — for example, all 671B parameters of DeepSeek-R1 fit in a single VM.

Oracle Cloud Infrastructure (OCI)

OCI is the only major cloud provider to offer bare metal instances with AMD GPUs, free of virtualization overhead. OCI offers AMD Instinct MI300X GPUs with 192 GB of memory at a competitive price of $6 per GPU-hour.

OCI is among the first industry leaders to adopt AMD open rack-scale AI infrastructure with AMD Instinct MI355X GPUs, and announced it will offer zettascale AI clusters with up to 131,072 MI355X GPUs.

AWS

AWS supports AMD EPYC-based instances and is part of AMD’s broad cloud expansion, though AMD Instinct GPU-specific instances are more prominently offered through Azure and OCI for ROCm workloads.

3. Specialty / Neo-Cloud Providers

DigitalOcean offers AMD MI300X and MI325X GPUs through its Gradient AI GPU Droplets, with on-demand pricing at $1.99/GPU/hour for MI300X (single GPU).

Other providers like RunPod, CoreWeave, Lambda Labs, and Vultr also increasingly offer AMD Instinct GPUs (MI300X, MI350X) with ROCm-compatible environments at competitive rates.

4. Local / On-Premise Hardware

AMD has rolled out a public preview of ROCm 6.4.4 that supports Radeon RX 9000 (RDNA 4) and RX 7000 (RDNA 3) GPUs natively on both Windows and Linux, as well as select Ryzen AI 300 “Strix” and Ryzen AI MAX “Strix Halo” APUs.

PyTorch on Windows and Linux is now available as a public preview, meaning you can run AI inference workloads directly on AMD hardware in the Windows environment — no workarounds, no dual-boot setups.

Summary Table

Platform AMD GPU Type
AMD Developer Cloud MI300X Cloud (free tier)
Microsoft Azure (ND MI300X v5) MI300X Cloud VM
Oracle Cloud (OCI) MI300X, MI355X Bare Metal / VM
DigitalOcean Gradient MI300X, MI325X Cloud GPU Droplets
RunPod / CoreWeave / Lambda MI300X, MI350X Specialty Cloud
Your own PC (Radeon RX 7000/9000) RDNA 3/4 consumer GPUs Local / On-Prem

ROCm Software Stack Notes

AMD has crossed the viability threshold with ROCm 6/7 — native PyTorch wheels, ROCm containers, and upstream support in vLLM/SGLang mean open-source stacks work “Day 0” if you standardize on ROCm images. MI300X (192 GB) and MI350X (288 GB HBM3E) match or exceed NVIDIA on per-GPU memory and are increasingly listed by neo-clouds.

References:

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