Google's Gemma 4 26B A4B is a workable local coding model for developers without dedicated GPU hardware, based on testing published by developer Luigi Aversano.
The model uses a Mixture-of-Experts (MoE) architecture - instead of running all 26 billion parameters for every response, it routes each token through just 4 billion active parameters. That design keeps memory usage manageable: at 4-bit quantization (a compression method that trades a small amount of accuracy for a smaller file size), the model fits in 16-18 GB of RAM. A MacBook Pro with 18-24GB unified memory can run it without a discrete GPU.
Benchmark numbers are solid for a local model: 82.6% on MMLU Pro (a multiple-choice test across academic and professional domains) and 88.3% on AIME 2026 (a competition mathematics benchmark). The model supports native tool-calling and a 256K token context window - roughly a 600-page book's worth of text - which matters for coding workflows where the model needs to read and edit multiple files in a single session.
OpenCode is an open-source, terminal-based coding assistant, similar in concept to Claude Code but fully self-hosted. Aversano tested Gemma 4 26B A4B within OpenCode and recommends disabling the model's built-in thinking mode for these workflows. Thinking mode adds a chain-of-thought reasoning step before each response - useful for complex logic problems, but it adds latency and compute cost that rarely pays off when the model is primarily calling tools and writing code.
The practical ceiling is RAM. 16GB is the minimum, and on machines where memory is shared between apps, headroom will be tight. The 8-bit version requires 28-30 GB, ruling out most laptops entirely.
For developers who want a self-hosted coding setup with no cloud API keys - and have a Mac or desktop with 18GB+ of RAM - this combination is one of the more capable local options currently available.