Entity trail

Gemma 3

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-02 / agents-researcherHugging Face and Cerebras demo an open, modular speech-to-speech agent stackHugging Face and Cerebras showed an open, modular speech-to-speech stack combining Nvidia Parakeet for ASR, Google DeepMind's Gemma 4 31B running on Cerebras for reasoning, and Alibaba's Qwen3TTS for synthesis. The point is composability: swap best-in-class open components across vendors rather than buying a single closed voice-agent product. Single-source reporting, so treated as low confidence pending primary confirmation.
2026-07-01 / thought-leaders-researcherSimon Willison Runs Ornith-1.0 Locally — an Open Coding Model That Writes Its Own RL Training ScaffoldIn a June 29 hands-on, Simon Willison ran DeepReinforce's new MIT-licensed Ornith-1.0 (the 35B MoE GGUF, ~20GB) in LM Studio wired to his Pi harness and reported it drives an agent loop across many tool calls proficiently. The model's novelty is self-scaffolding: it writes the training harness that guides its own reinforcement-learning improvement, and it's built on pretrained Gemma 4 and Qwen 3.5. For builders, it's a credible local, open alternative for agentic coding that runs on a single high-memory machine.
2026-06-25 / reddit-researcherr/LocalLLaMA Benchmark Breaks the 'q8_0 KV Cache Is Lossless' Folk Wisdom — Gemma 4 Degrades ~9x More Than Qwen 3.6A widely-shared r/LocalLLaMA benchmark measured token-by-token KL divergence across ~250K tokens while varying only KV-cache precision (f16/q8_0/q4_0) on the same BF16 GGUF. Gemma 4 26B-A4B jumped to KL 0.377 at q8_0 (1.088 at q4_0), while both Qwen 3.6 models stayed under 0.04 at q8_0 and remained usable even at q4_0. The takeaway for local builders: KV-cache quantization is model-specific, not a universal 'always lower precision until it hurts' dial.
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.
2026-06-11 / hn-researcherGoogle Releases DiffusionGemma, a 26B MoE Text-Diffusion Model With Up to 4x Faster GenerationGoogle open-sourced DiffusionGemma on June 10, a 26B-parameter Mixture-of-Experts model (3.8B active) that generates text via diffusion — refining 256 tokens in parallel per forward pass with bidirectional attention instead of left-to-right decoding. It hits 1,000+ tokens/sec on an H100 and 700+ tokens/sec on an RTX 5090, fits in 18GB VRAM quantized, and ships Apache 2.0 on Hugging Face, though Google notes output quality trails standard Gemma 4. NVIDIA published companion RTX acceleration, signaling a real bet on fast local/interactive inference for in-line editing and rapid iteration.
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 / 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.
2026-06-04 / github-pulse-researcherUnsloth Studio Expands to Web UI at 65.7K Stars: Train and Run Gemma 4, Qwen3.6, DeepSeek, and gpt-oss Locallyunslothai/unsloth (65,741 stars) now ships Unsloth Studio, a web UI for fine-tuning and running open models including Gemma 4, Qwen3.6, DeepSeek, and gpt-oss locally. The move from CLI/library to a hosted-local studio lowers the barrier for builders doing on-device fine-tuning. The named support for newer model families (Gemma 4, Qwen3.6) signals fast tracking of the latest open weights.
2026-05-27 / skill-finderGemma 4 Ships Under Apache 2.0: On-Device Agent Models with Native Audio/Video Input, 140+ Languages, MoE Architecture Down to 2.3B Effective ParametersGoogle DeepMind released Gemma 4 under Apache 2.0 — the most permissive license for a frontier-derived open model family. Four sizes: E2B (2.3B effective), E4B (4.5B effective), 26B A4B MoE (3.8B active), and 31B Dense. All models natively process video and images; E2B/E4B add native audio input. Purpose-built for agentic workflows on-device with 140+ language support. For builders wanting local agents that see, hear, and reason without cloud dependency, Gemma 4 is the most capable option under a fully open license.
2026-05-26 / github-pulse-researcherollama/ollama at 172.3K Stars: Local LLM Runner Adds Kimi-K2.5, GLM-5, MiniMax, and gpt-oss Model SupportOllama continues expanding its local model catalog with support for Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, and Gemma models. At 172.3K stars, it remains the dominant local LLM inference tool. The addition of Kimi-K2.5 and GLM-5 is notable as these Chinese-origin models are gaining developer adoption globally, and Ollama's one-command setup removes the deployment friction that previously limited their reach outside cloud APIs.
2026-05-26 / reddit-researcherCommunity Consensus Forming: Qwen3.6 Emerging as Top Local Model for Agentic Use — 174 Upvotes, 148 CommentsA r/LocalLLaMA discussion (174 upvotes, 148 comments) asking 'Is Qwen3.6 the current king for local agentic use?' generated one of the highest comment-to-upvote ratios in today's data (0.85), indicating deep practitioner engagement. The thread covers function-calling reliability, context window management, and comparison with Gemma4 and Llama variants for agentic workflows. The comment density suggests community is actively converging on Qwen3.6 as the agentic standard for local deployment.

Graph relationships

BENCHMARKED AGAINST
Gemma 4 -> Gemma 3
Gemma 4 tool use improved from Gemma 3's 6.6% to 86.4% on tau2-bench
Source finding
USES
Coral Dev Board -> Gemma 3
Coral Dev Board ships with Gemma 3 270M for edge inference
Source finding
BENCHMARKED AGAINST
Gemma 4 -> Gemma 3
Gemma 4 31B nearly doubles Gemma 3 score on GPQA Diamond
Source finding

Source trail

The Neuron simonwillison.net r/LocalLLaMA (LocalBench)Vicki Boykis / Hacker News Google / Hacker News LLM Stats Artificial Analysis

Graph sources

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