Updated
Updated · KDnuggets · Jun 10
Shittu Olumide Builds 26B Local Coding Stack With Ollama and Claude Code
Updated
Updated · KDnuggets · Jun 10

Shittu Olumide Builds 26B Local Coding Stack With Ollama and Claude Code

1 articles · Updated · KDnuggets · Jun 10

Summary

  • A new guide lays out a full local agentic programming setup using Ollama, Google DeepMind’s Gemma 4 26B MoE and Claude Code, aimed at avoiding per-token API costs, privacy leakage and rate-limit disruptions.
  • Gemma 4 is the key enabler: the 26B MoE activates 3.8 billion parameters per pass, scores about 79% on τ2-bench tool use and 77.1% on LiveCodeBench v6, versus 6.6% and 29.1% for Gemma 3 27B.
  • The walkthrough centers on practical fixes that make local agents usable, including overriding Ollama’s default 4K context to 64K, setting Claude Code to localhost:11434, and using a verification script to test health, API calls and tool_use output.
  • Hardware remains the main constraint: the recommended model needs roughly 16-18 GB VRAM and an 18 GB download, while the article says cloud models still outperform on large-scale architectural reasoning and SWE-bench-style tasks.
  • The broader pitch is that Apache 2.0-licensed Gemma 4 and Ollama’s Anthropic-compatible API now make a private, zero-per-token coding agent practical for everyday work such as code analysis, test generation, refactoring and debugging.

Insights

With powerful AI agents running locally, what new security risks emerge beyond corporate firewalls?
As local AI rivals cloud giants, will big tech's API dominance finally crumble?
Is the shift to local AI trading expensive tokens for even costlier hardware and maintenance?