Google DeepMind releases Gemma 4 under Apache 2.0 with 31B Dense, 26B MoE, and 256K context

• Google DeepMind shipped four Gemma 4 models under an Apache 2.0 license, with 31B Dense, 26B MoE, and two edge variants aimed at phones and laptops Launch thread Logan Kilpatrick post.
• The bigger models push to 256K context for codebases and long documents, while the E2B and E4B edge models top out at 128K and add native audio input Agentic capabilities Reddit hardware breakdown.
• Google is pitching Gemma 4 as an agent model, with native tool use, structured JSON output, multimodal input, and function calling for local workflows Agentic capabilities Launch thread.
• Hugging Face's launch post adds the technical details missing from the tweets: variable-aspect image handling, configurable image token budgets, per-layer embeddings, and a shared KV cache for long-context efficiency in transformers, llama.cpp, MLX, WebGPU, and Rust stacks Hugging Face release.
• Early community reports were enthusiastic about the license change and local potential, but practical context limits still depended on runtime and quantization, with one Reddit user saying some Gemma 4 quants only ran at 32K or 64K on a 16GB GPU before they rolled back to Qwen Apache 2.0 reaction Reddit thread.

You can read Google's post, jump straight to the Hugging Face technical writeup, and browse the Kaggle release. There is also day-0 support in Google AI Studio, vLLM, and a long Hacker News thread full of quantization tips and local deployment chatter.

The biggest change is the license. Google says Gemma 4 is released under Apache 2.0, and Hugging Face treated that as launch-day headline material too Apache 2.0 reaction.

That puts Gemma 4 on the same legal footing as the open model families developers already redistribute, fine-tune, and bundle into products. For a model line that had already reached 400 million downloads and more than 100,000 variants, according to Google's post, that is the part likely to travel furthest.

Google split the family into four tiers:

• E2B: 2.3B effective parameters, 128K context, built for edge hardware.
• E4B: 4.5B effective parameters, 128K context, also aimed at low-memory local use.
• 26B A4B MoE: 26B total parameters, about 4B active during inference, 256K context.
• 31B Dense: 31B dense model, 256K context, positioned as the highest-quality checkpoint.

The company says the 31B model ranks third on Arena AI's open text leaderboard, with the 26B model sixth, and the launch chart argues both compete with far larger open models Benchmark chart. Hugging Face's table matches the context split and notes that all four ship in base and instruction-tuned variants.

Google's own pitch is less chatbot, more local agent. The launch thread calls out app navigation, database search, API triggering, and long action histories, all with native tool use Agentic capabilities.

The Hugging Face post fills in the mechanics:

• All models take text and images, or video frames, as input.
• E2B and E4B also take audio input.
• The vision stack preserves variable aspect ratios.
• Image token budgets are configurable at 70, 140, 280, 560, or 1120 tokens.
• The models support JSON output and multimodal function calling.
• Hugging Face reports good informal results on OCR, GUI detection, object pointing, speech-to-text, and HTML reconstruction from screenshots.

That last part is catnip for creative tool builders. Google is shipping a model family that can look at interfaces, emit structured coordinates, and keep enough context around to act on them.

Distribution was broad on day one. Google pushed Gemma 4 into AI Studio, while also offering weights through Hugging Face, Kaggle, and Ollama Availability post. Hugging Face says it worked with Google and the community to land support across transformers, llama.cpp, MLX, WebGPU, and Mistral.rs, while vLLM added day-0 support across TPUs, AMD GPUs, and Intel XPUs.

The first community reports already showed the usual local-model split between theory and runtime reality. One HN commenter from Unsloth shared quantization guidance and sampling settings for local runs, while a Reddit user with a 16GB RTX 5060 Ti said larger Gemma 4 quants only became usable after shrinking context windows, with some landing at 64K or 32K instead of the advertised maximum Reddit thread. Another commenter in the same thread framed the smaller models as routing and RAG helpers, not frontier replacements.

That makes Gemma 4 feel less like a single model launch and more like a new local stack to benchmark carefully, especially if the interesting use case is multimodal agents on hardware you already own.