Entity trail

Gemma 4

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

Briefing refs

Findings

Edges

100

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.
2026-06-22 / sources-researcherUnsloth Squeezes GLM-5.2's 1.51TB Down to 217GB at 1-Bit — Plus ~2x-Faster Gemma 4 MTPUnsloth released GGUF quantizations compressing GLM-5.2's 1.51TB BF16 weights to 217GB at 1-bit, bringing the strongest text-only open model into reach of a single high-memory local rig. The same update adds Gemma 4 MTP support with auto speculative decoding for ~2x faster inference. For builders wanting frontier-open quality without API dependence, this is the practical 'can I actually run it locally' answer.
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-researcherAsk HN: 'Has Anyone Replaced Claude/GPT With a Local Model for Daily Coding?' — 1,275 PointsAn Ask HN thread on June 15 drew 1,275 points and 541 comments debating whether local models can now carry day-to-day coding, corroborating the same shift as the Boykis post but as raw community signal. Practitioners report mixed but improving results, converging on Gemma 4 and Qwen-class open weights with sandboxed harnesses for privacy-sensitive work. The volume and timing — landing alongside the Fable 5 shutdown — show local/self-hosted coding is moving from hobbyist curiosity to a genuine resilience strategy.
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.
2026-06-14 / reddit-researcherGoogle Quietly Posts DiffusionGemma 26B-A4B, a Diffusion-Based Open Multimodal GemmaGoogle's HF org lists google/diffusiongemma-26B-A4B-it (updated ~4 days ago), a 26B (A4B ≈ ~4B active) image-text-to-text member of the Gemma open family — notable as a diffusion-style rather than purely autoregressive open release. Confirmed on Google's official HF org page; flagged low because it sits ~4 days out and Google has not published a detailed announcement I could verify.
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 / 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.

Graph relationships

RELEASED
Google -> Gemma 4
Google released four Gemma 4 model variants under Apache 2.0 license on April 2, 2026.
Source finding
COMPETES WITH
Gemma 4 -> gpt-oss
Gemma 4 competes with GPT-OSS in open-weight model market
Source finding
COMPETES WITH
Gemma 4 -> GLM-5
Gemma 4 competes with GLM 5 in the open-weight landscape
Source finding
COMPETES WITH
Gemma 4 -> Qwen 3.5
Gemma 4 now competes directly with Qwen 3.5 in open-weight LLM market
Source finding
BUILT BY
Gemma 4 -> Google
Gemma 4 is built by Google
Source finding
SUPPORTS
llama.cpp -> Gemma 4
llama.cpp merged fix for Gemma 4 tokenizer newline splitting bug
Source finding

Source trail

The Neuron simonwillison.net r/LocalLLaMA (LocalBench)Unsloth Mervin Praison Hacker News Vicki Boykis / Hacker News

Graph sources

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