gemma-4 codex-cli local-ai-models code-generation

I ran Gemma 4 as a local model in Codex CLI

blog.danielvaughan.com

April 12, 2026

7 min read

🔥🔥🔥🔥🔥

61/100

Summary

Gemma 4 was tested as a local model in Codex CLI to determine its viability as a replacement for cloud models in coding tasks. The testing involved using a 24 GB M4 Pro MacBook Pro to evaluate Gemma 4's local tool calling capabilities.

Key Takeaways

Gemma 4 achieved an 86.4% success rate on the tau2-bench function-calling benchmark, significantly improving its tool-calling capabilities compared to previous versions.
The author ran Gemma 4 locally on two different machines, a MacBook Pro and a Dell Pro Max, to evaluate its performance against cloud models.
Local models like Gemma 4 offer advantages in cost, privacy, and resilience compared to cloud-based APIs.
Setting up Gemma 4 required troubleshooting various bugs and configuration adjustments, particularly with the Codex CLI and model compatibility.

Read original article

View story →

Coverage & discussions

Hacker News · blog.danielvaughan.com

I ran Gemma 4 as a local model in Codex CLI ↗

Community discussions

Hacker News discussion ↗

Historical count: 5 · 5 saved in sample · Historical sample

Analysis ready

Community Sentiment

Mixed

Positives

Running Gemma 4 on local hardware like the M3 Ultra demonstrates the model's capability to handle large context sizes effectively, which is crucial for complex tasks.
The integration of Gemma 4 with tools like Zed via ACP showcases its versatility and ease of use in real-world applications.
Users report significant performance improvements with the M5 Pro, indicating that hardware upgrades can lead to enhanced model efficiency and speed.
The ability to offload MoE tasks to the CPU allows for flexibility in managing context size and token generation speed, which can optimize performance.

Concerns

Concerns about the quality of fine-tuned models compared to original versions suggest skepticism about performance claims, highlighting the need for thorough benchmarking.
Issues with tool calling in models like Qwen 3.5 indicate that practical challenges remain, which can hinder user experience and effectiveness in coding tasks.
The necessity of increasing context size to achieve useful results points to potential limitations in the model's default configurations, which may not meet all user needs.