If you haven't yet given Gemma 4 a go...do it today
A user reports Gemma 4's speed and coding accuracy rival early Gemini Pro, marking a major leap for local LLMs.
Google's latest open-weight model, Gemma 4, is generating significant buzz in the developer community for its exceptional performance-to-speed ratio. According to a detailed user report from someone running models locally via Ollama, the 26-billion-parameter Gemma 4 operates with the speed typically associated with much smaller 4B or 9B models. This breakthrough in inference efficiency means users with modest hardware setups can now access a far more capable model without the traditional trade-off in response time.
Beyond raw speed, users are praising the model's accuracy and confidence, particularly in code generation. The experience is being compared to the initial release of Google's Gemini Pro, which was notable for producing executable code. For developers and security professionals who prefer self-hosted, local AI solutions, this combination of speed, size, and capability represents a major step forward in usability, potentially challenging other popular local models like Qwen 3.5 27B. Early testing across diverse tasks—including legal interpretation, Python programming, and brainstorming—shows promising results, especially when using the recommended model settings and specific versions like `bjoernb/gemma4-26b-fast:latest`.
- Runs at speeds comparable to 4B-9B models despite being a 26B parameter model, a major efficiency leap.
- Delivers code generation accuracy and confidence that users compare to the early, capable release of Gemini Pro.
- Enables high-performance local AI on modest hardware, changing the usability calculus for self-hosted LLMs.
Why It Matters
Democratizes powerful AI development by making a highly capable model fast and accessible for local, private deployment on standard hardware.