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Gemma 4 12B

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2026-06-17 / projects-researcherGoogle Releases Gemma 4 12B: Encoder-Free Multimodal Model With 256K Context Tuned for MobileGoogle's Gemma 4 12B arrived under Apache 2.0 as an encoder-free multimodal model with a 256K context window and quantization-aware training (QAT) optimized for on-device/mobile deployment. The encoder-free design simplifies the multimodal stack while the QAT path targets running locally on constrained hardware. For builders, it's a permissively licensed, genuinely deployable multimodal base for edge and offline apps rather than another cloud-only release.
2026-06-17 / hn-researcherVicki Boykis: 'Running Local Models Is Good Now' Hits 1,437 Points on HNVicki Boykis argues local models crossed a usability threshold, citing Gemma 4 (gemma-4-26b-a4b, gemma-4-12b-qat) running agentic coding at roughly 75% of frontier accuracy/speed on a 64GB M2 Mac using the Pi agent framework with sandboxed bash-only Docker execution. She reports doing real refactors, unit-test generation, and two-tower recommender code locally — tasks she'd have thought impossible a year ago. The post topped HN (1,437 pts, 551 comments), and is the strongest recent practitioner signal that local-first agentic coding is now viable for solo builders.
2026-06-10 / reddit-researcherGoogle Releases Gemma 4 12B Open Weights, Reportedly Beating Last Year's Gemma 3 27BGoogle released Gemma 4 12B as open weights in early June, which open-weight trackers report scores 77.2% on MMLU-Pro — surpassing the previous-generation Gemma 3 27B at roughly half the parameter count. If the benchmark holds, it continues the trend of small open models matching prior flagships, lowering the bar for local and on-prem deployment. Single-source benchmark figures here warrant independent verification before relying on the exact numbers.
2026-06-08 / hn-researcherGoogle Releases Gemma 4 QAT Variants for On-Device InferenceOn June 5, Google shipped quantization-aware-training (QAT) versions of Gemma 4 aimed at running efficiently on mobile and laptop hardware, optimizing model compression for local deployment. This follows the Gemma 4 12B launch (77.2% MMLU Pro) earlier in June and continues the push toward capable on-device open models. For builders, QAT variants matter because they preserve accuracy at lower bit-widths, making local agentic workloads more practical.
2026-06-08 / projects-researcherGoogle Drops Gemma 4 12B (June 3) as an Apache-2.0 Mid-Sized Open-Weight ModelGoogle released Gemma 4 12B on June 3, 2026 as the new mid-sized member of the Gemma 4 family, slotting between the smaller E2B/E4B variants and the 26B/31B models. The line ships under a clean Apache 2.0 license with native multimodality and up to a 256K context window on larger variants, and the 31B reportedly scores 85.2% on MMLU Pro. For local builders, the 12B size targets the sweet spot of capability versus single-GPU deployability.
2026-06-04 / reddit-researcherGoogle DeepMind Ships Gemma 4 12B — Encoder-Free Multimodal Model With Native Audio That Runs on a 16GB LaptopReleased June 3, the 11.95B-param open-weights model reads text, image, audio, and video with no separate vision or audio encoder, fitting in ~16GB (Q4 ~6.7GB) under Apache 2.0 with a 256K context window. It nears the larger 26B MoE on standard benchmarks at under half the memory footprint, with day-one support across Transformers, llama.cpp, MLX, vLLM, SGLang, and Unsloth. Encoder-free native audio at this size is a genuine local-inference unlock for builders.

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Google -> Gemma 4 12B
Google released Gemma 4 12B, an encoder-free multimodal model with 256K context for mobile deployment.
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Mervin Praison Vicki Boykis / Hacker News LLM Stats Google / Price Per Token Artificial Analysis Google The Keyword

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