Gemma 4

Google Gemma 4 is Google’s 2026 open-weight model family for developers who want local inference, OpenAI-compatible APIs, multimodal inputs, and agentic workflows without defaulting every task to a frontier cloud model. Start with Gemma 4 12B for laptops, use E2B or E4B for edge devices, and move to vLLM, Vertex AI, or GKE when throughput and operations matter. What Is Google Gemma 4 in 2026? Google Gemma 4 is an Apache 2.0 open-weight model family from Google designed for local, edge, and cloud AI applications, with five published sizes: E2B, E4B, 12B, 26B A4B, and 31B. The 2026 release matters because Google reports more than 150 million Gemma downloads by June 3, 2026, and the model card lists text and image input across the family, audio support on E2B, E4B, and 12B, and context windows up to 256K tokens on the larger models. For developers, Gemma 4 is not just a chat model; it is a practical base for local code assistants, retrieval pipelines, structured extraction, and privacy-sensitive internal tools. The main takeaway: Gemma 4 is useful when you want capable open models with deployment choices from phones to managed Google Cloud infrastructure. ...

Gemma 4 is Google’s family of open-weights models released April 2, 2026 under Apache 2.0 — four sizes from a 2B mobile-ready model to a 31B dense powerhouse, all runnable locally without sending a single byte to Google’s servers. This guide covers every deployment path: Ollama, LM Studio, Hugging Face Transformers, llama.cpp, Android, and iOS. What Is Gemma 4 and Why Run It On-Device? Gemma 4 is Google DeepMind’s fourth-generation open-weights language model family, released on April 2, 2026 under the Apache 2.0 license with no commercial restrictions. The family spans four sizes — E2B (~2.3B effective parameters), E4B (~4.5B), 26B MoE (only 3.8B active per token), and 31B Dense — each capable of running entirely on consumer hardware. At the top end, the 31B model scores 85.2% on MMLU Pro and 81.8% on HumanEval; the 26B MoE model sits at Arena AI ELO rank #3 globally at 1452 — all while being something you can run on a gaming laptop. Running Gemma 4 on-device eliminates API costs entirely, replacing per-token billing with a one-time GPU investment. More importantly, inference stays local: code, documents, customer data, and proprietary context never leave your machine. For enterprises bound by HIPAA, SOC 2, or internal data governance rules, that’s not optional — it’s the whole point. Apache 2.0 also means you can fine-tune on proprietary data and redistribute the result commercially, without any restrictions that come with Meta’s Llama license or Mistral’s community terms. ...

Gemma 4 31B scores 89.2% on AIME 2026 — a 330% improvement over Gemma 3 27B’s 20.8% — while Qwen3-235B-A22B leads on GPQA Diamond at 77.2% and Llama 4 Scout holds the record with a 10 million token context window. Three competitive open-source model families launched in 2026, each with distinct architectural advantages that make the choice non-obvious. Gemma 4 leads on reasoning-per-parameter efficiency. Llama 4’s Scout model offers an unmatched context window for processing entire codebases. Qwen 3 provides the strongest raw coding performance at full size. This guide covers the technical and practical differences for developers choosing which family to run locally or deploy in production. ...

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. ...