Gemma-4 26B-A4B with Opencode Runs Efficiently on M5 MacBook Air
A developer tested Gemma-4-26B-A4B with Opencode on a 32GB M5 MacBook Air and found it delivers practical performance for local AI coding tasks.
Performance Benchmarks
The specific configuration tested was gemma-4-26B-A4B-it-UD-IQ4_XS running on a 32GB M5 MacBook Air. In low power mode, it achieved:
- 300 tokens/second prompt processing
- 12 tokens/second generation
- 8W power consumption
- No heat or fan noise during operation
The M5 MacBook Air showed significant improvements over previous hardware:
- ~25% faster prompt processing than an M1 Max 64GB (even when the Max wasn't in power saving mode)
- ~6 hours of battery life versus ~2 hours on the M1 Max when running Opencode
- This despite having a smaller battery (53.8Wh vs 70Wh on the M1 Max)
Practical Use Cases
The developer found this setup "actually usable" for agentic coding behavior from a laptop. Previously, running LLMs on an M1 Max 64GB was limited to "tinkering and toy use cases" and couldn't handle longer context tasks effectively. While it could create a simple Snake game in Python, agentic coding or contributing to larger codebases was "a bit janky."
The M5's performance makes it practical for mobile use cases where internet connectivity might be unreliable, such as coffee shops or train commutes.
Comparison to Other Models
The developer compared Gemma-4-26B with Opencode to closed-source alternatives:
- It doesn't replace Claude Code or Antigravity from their testing
- Gemma-4 requires "far more hand-holding than current closed-source frontier models"
- The responses are described as "kinda dry" compared to Claude Code or Gemini-3.1-Pro with Antigravity
- However, they'd prefer Gemma-4-26B over running out of Gemini-2.5-Pro allowance and being forced to use Gemini-2.5-Flash
The developer notes this represents significant progress, as "this sort of agentic coding was cutting-edge / not even really possible with frontier models back at the end of 2024."
📖 Read the full source: r/LocalLLaMA
👀 See Also
Caliby: Open-Source Embedded Vector Database for AI Agents with Hybrid Text+Vector Storage
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Moving from CLAUDE.md rules to infrastructure enforcement with Citadel
A developer found that adding more rules to CLAUDE.md beyond about 100 lines reduced compliance, with 40% redundancy in their file. The solution was moving enforcement from instructions to infrastructure using lifecycle hooks, skills, and campaign files, culminating in the open-source Citadel system.
ToolLoop: Open-Source Framework for Claude-Style Tools with Any LLM
ToolLoop is an open-source Python framework with 11 tools for file operations, code search, shell access, and sub-agents that works with any LLM through LiteLLM. The 2,700-line framework allows switching models mid-conversation while maintaining shared context.
Detrix MCP Server Adds Runtime Debugging to AI Coding Agents
Detrix is a free, open-source MCP server that enables MCP-compatible agents to observe live variables in running code without restarts or code changes. It supports Python, Go, and Rust applications running locally or in Docker.