Gemma 4

Gemma 4 31B Dense runs locally on a single RTX 4090 or Mac M3 Max using Ollama — no API key, no data leaving your machine. Install Ollama, run ollama pull gemma4:31b, and you have a model that scores 87.1% on MMLU, beating GPT-4o’s 86.5%, running entirely on your hardware. What Is Gemma 4 31B Dense and Why Run It Locally? Gemma 4 31B Dense is a 31-billion-parameter language model released by Google DeepMind on April 2, 2026, under the Apache 2.0 license. Unlike mixture-of-experts architectures that distribute parameters across sparse expert layers, the 31B Dense model activates all 31 billion parameters on every token — giving it more reliable reasoning depth than larger MoE models with similar active parameter counts. In benchmark testing, Gemma 4 31B scores 87.1% on MMLU (beating GPT-4o’s 86.5%), 89.2% on AIME 2026, and 84.3% on GPQA Diamond — outperforming Llama 4 Scout’s 109B MoE model on the harder science benchmarks. Running it locally means zero API costs, complete data privacy, no rate limits, and the ability to integrate with any tool via the OpenAI-compatible REST endpoint that Ollama exposes on localhost:11434. For developers, researchers, or privacy-conscious users, this is the highest-performing open model available for on-device inference as of mid-2026. ...

Gemma 4 is Google DeepMind’s 2026 open-source model family — four model sizes from 2B (phone-optimized) to 31B dense, all under Apache 2.0, scoring 89.2% on AIME 2026 and ranking #3 on the Arena AI leaderboard. If you’re evaluating open-weight models for production use today, Gemma 4 is the most commercially viable and technically competitive option available. What Is Gemma 4? Google’s Open-Source Flagship Explained Gemma 4 is Google DeepMind’s fourth-generation open-weight language model family, released on April 2, 2026, designed to cover the full deployment spectrum — from on-device inference on smartphones to large-scale server workloads. Unlike prior Gemma generations, Gemma 4 ships with genuine frontier-model performance: the 31B dense variant scores 84.3% on GPQA Diamond, outperforming Meta’s Llama 4 Scout (109B) at 74.3%, and reaching 89.2% on the AIME 2026 math benchmark — a figure that was 20.8% just one generation earlier. The model family is multimodal (vision + audio input on edge models), multilingual (140+ languages), and supports context windows up to 256K tokens. Since Google’s first Gemma release, developers have downloaded Gemma models over 400 million times, and the Gemmaverse now includes over 100,000 community-created fine-tunes and variants. That ecosystem depth means production-grade LoRA adapters, GGUF quants, and tool integrations are available day one — not months later. Gemma 4 is the model to benchmark any other open-weight model against in 2026. ...