AMD AI PRO R9700 32GB Review: AMD’s New RDNA 4 GPU for AI Workloads

AMD RDNA 4 GPU review showing AI-focused performance, setup challenges on Windows, and value compared to Nvidia workstation cards.

Hardware by Tanvir Kabbo on Sep 25, 2025

AMD has recently launched its newest RDNA 4 graphics card, specifically designed for AI workloads. The company claims that its product is half the cost of its main competitor, the RTX Pro 4500, and boasts performance that is twice as fast as the previous generation.

Initial impressions suggest high potential, but some practical challenges exist when setting it up for AI tasks.

AMD, AI PRO R9700 32GB, Review, AMD New RDNA 4, GPU for AI Workloads, NoobFeed

Initial Setup Challenges

The GPU doesn't work with a lot of AI tools on Windows right out of the box. It was easy to make text with LM Studio on Windows, but it was hard to set up image generation with Comfy UI or Stable Diffusion Web UI.

Most AMD AI applications, including drivers, suggest Linux as the primary environment, which adds complexity for users unfamiliar with the platform. In comparison, Nvidia GPUs work seamlessly on Windows without requiring Linux for setup.

Hardware Specifications

The graphics card is essentially an upgraded version of the 9700 series, with double the VRAM but the same die. It comes with a two-slot blower-style cooler, which is ideal for multi-GPU setups. The GPU features a 300W TDP, supports ECC memory on Linux, and includes DisplayPort 2.1 with AV1 decode and encode capabilities.

A limitation of the card is that it utilizes older GDDR6 memory on a 256-bit bus, although testing will determine the actual impact of this specification in real-world scenarios. According to ASRock, the card can deliver 1531 AI TOPS, suggesting it should perform similarly or even better than an RTX 4090 in certain AI workloads.

The test system features a Ryzen 5800X3D processor, 128GB of DDR4 RAM at 3200MHz, and a 2TB SSD. For comparison purposes, Mac results are also noted.

Performance and Heat Considerations

If you plan to use the GPU for a long time, keep in mind that it gets very hot when it's under load. The two-slot setup is still a challenge for demanding AI tasks, even though it helps with cooling.

AMD, AI PRO R9700 32GB, Review, AMD New RDNA 4, GPU for AI Workloads, NoobFeed

Practical Use Cases

This card isn't the ideal choice for gaming. If your main goal is to play games, a 16GB device would be cheaper and work just as well. This GPU excels in AI-focused tasks, but be prepared to navigate setup challenges on Windows.

AMD's AI image generation tools are available, but they are not immediately visible in the driver package or box contents, leaving users initially confused.

Using Linux, we successfully ran LM Studio, Invoke AI, and Comfy UI; however, the process involved navigating several compatibility issues, including limitations with AppImage on Ubuntu 24 LTS and risks associated with Fuse installation.

In comparison, Nvidia GPUs offer a more straightforward experience, eliminating the need to deal with ROCM versions or Linux-specific dependencies.

AI Performance

This GPU excels in text generation and is a strong performer when using AMD-optimized models. It offers substantial memory, allowing AI training and image generation tasks without requiring an extreme power supply upgrade.

However, video generation experiments highlighted limitations, including errors about 8-bit process support and conflicting ROCM version requirements.

Value Comparison

RTX Pro Blackwell 4500 is more than twice as expensive despite having comparable performance. The RDNA 4 card is a great value for individuals interested in AI, particularly in text generation. Implementing multi-GPU systems without significant power issues is made easier by its small two-slot design and reasonable 300W TDP.

AMD, AI PRO R9700 32GB, Review, AMD New RDNA 4, GPU for AI Workloads, NoobFeed

Final Thoughts

AMD RDNA 4 GPU is a professional workstation card rather than a gaming-class GPU like the 4090 or 5090. It offers strong AI capabilities at a competitive price, particularly for text generation tasks.

However, users should be aware of setup complexities on Windows and Linux. Beginners may find Nvidia alternatives easier for immediate AI deployment, but the memory capacity and performance of this card make it a solid choice for dedicated AI workflows.

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