I recently joined the AMD AI Hackathon to explore building AI applications using high-performance GPU infrastructure.

I went in mostly to experiment and learn, but I’ve ended up picking up a few interesting lessons that I didn’t expect.

A few things that stood out so far:

1. GPU performance changes how you think about AI workflows
When you’re not just running models locally on a CPU, but actually deploying and optimizing for GPUs, you start thinking differently about efficiency, batching, and compute limits. Even small design choices matter more.

2. The ecosystem is powerful, but not always beginner-friendly
The tooling and infrastructure (especially around GPU setup and deployment) is powerful, but there’s a learning curve. A lot of time goes into just understanding how everything connects before you even build the “real” AI part.

3. Hackathons are underrated for learning fast
Even if you don’t “win,” the speed at which you’re forced to learn, break things, and rebuild is probably the most valuable part. I’ve learned more in a short time here than I would have just reading tutorials.

Where I’m at now

I’m still actively building and iterating on my project, and trying to better understand how to design AI systems that actually take advantage of GPU compute rather than treating it as an afterthought.

Curious about others here:

• Have you worked with AMD GPUs or similar setups?
• How do you approach optimizing AI workloads for GPU infrastructure?
• Any lessons you wish you knew earlier when starting with high-performance AI systems?