The BEST Local LLM for opencode ! Gemma 4 26B A4B. No GPU required

Google's new Gemma 4 26B A4B is making waves in the local LLM community. This Mixture-of-Experts model only activates 4B parameters out of 26B, making it surprisingly fast on consumer hardware, even on a MacBook without a GPU.
In a new video, the model is tested with opencode to answer real-world questions like checking Linux versions. The results? Pretty impressive. It handled tool usage, searched for information, and responded accurately, all without an online LLM API.

Why It Matters

Gemma 4 comes in four sizes: E2B, E4B, 26B-A4B, and 31B. The 26B-A4B variant is the sweet spot for local use because:

• Fast inference: Only 4B parameters active per token
• Long context: Up to 256K tokens
• Tool calling: Native support for function calls
• Reasoning mode: Optional "thinking" mode for complex tasks

Hardware Requirements

According to Unsloth docs, here's what you need:

Benchmarks

The model scores well on reasoning and coding benchmarks:

• 26B A4B: 82.6% MMLU Pro, 88.3% on AIME 2026
• 31B: 85.2% MMLU Pro, 89.2% on AIME 2026
  The 31B is slightly stronger but slower. For opencode use, the 26B-A4B hits the right balance.

The Catch

Disable thinking mode for agentic workflows. It saves compute and still gives correct answers for most tasks. Only enable it for math or reasoning-heavy questions.

Sources

• Unsloth Gemma 4 Documentation - Hardware requirements and benchmarks
• LM Studio Gemma 4 - Model specifications
• Google Gemma 4 Model Card - Official specs

Want to try it? Check the video for a full demo with opencode integration. The model works with llama.cpp and runs locally without sending data to external APIs, which is good for privacy.