Gemma 4 MoE for Agentic Coding: Testing Open-Weight Models on AMD APU Hardware

Alex Ewerlof has written up what it's actually like to use local models for coding work, prompted by cloud pricing moving past its introductory phase. He ran Gemma 4 variants on an AMD Ryzen 7 PRO 250 APU with 64GB of RAM, driving them through LM Studio with a 150k context window. The concrete result he reports is Gemma 4 26B MoE generating a working Snake game.

His conclusion is measured rather than triumphant: open-weight models underperform the flagship proprietary ones, but in his words, "with tooling (e.g. an AI harness) you can compensate a lot." The drawbacks he names are cold-start delay and slower inference than a hosted API — the familiar cost of keeping the model on your own machine.

Worth reading for the setup detail rather than the verdict. An APU with 64GB of shared memory is a different proposition from a discrete GPU, and the combination of that hardware, a 150k context and an agent harness is a specific configuration you can compare your own against. One generated game is a demonstration, not a benchmark, so treat the capability claims as a starting point for your own testing rather than a settled result.

Read the full article on blog.alexewerlof.com.

Source: blog.alexewerlof.com · Relevance: 8/10