Entity trail

Gemma 4 31B

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

Briefing refs

Findings

Edges

Sources

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.
2026-06-08 / 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.
2026-05-27 / 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.
2026-05-17 / hn-researcherApple Silicon LLM Inference Costs ~3x More Than OpenRouter — Counter-Intuitive Energy and Cost Analysis — 119 PointsA detailed cost analysis published today (May 17) shows that running LLMs locally on Apple Silicon amortizes to approximately $1.50 per million tokens when accounting for hardware depreciation, while OpenRouter offers equivalent models (Gemma4 31b) at $0.38–0.50 per million tokens at 3-7x the speed (60-70 tok/s vs 10-20 tok/s). The Apple M3 Ultra delivers 23x more tokens per joule than an RTX 5090, but hardware cost dominates the equation. Privacy and offline use remain the primary justifications for local inference.
2026-05-17 / reddit-researcherGemma 4 Ortenzya: Writing-Focused Finetune Aims to Fix Prose Quality and TranslationA second Gemma 4 31B creative finetune dropped on HuggingFace targeting natural English prose, creative writing, translations, and roleplay. Available in Safetensors and GGUF, the 55-upvote release (22 comments) focuses specifically on improving writing quality where base Gemma 4 falls short, rather than just removing refusals like other uncensored variants.
2026-05-17 / reddit-researcherG4-Meromero-31B: Gemma 4 Uncensored Creative Finetune Drops with 15/100 Refusal RateA new finetune of Google's Gemma 4 31B designed for creative tasks launched on HuggingFace, achieving a KL divergence of just 0.0100 from the base model with only 15/100 refusals. Available in both Safetensors and GGUF formats, the 87-upvote post (38 comments) signals the Gemma 4 ecosystem is rapidly developing uncensored creative variants alongside the Qwen ecosystem.
2026-05-06 / hn-researcherGoogle Releases Multi-Token Prediction Drafters for Gemma 4 — Up to 3x Faster Open-Source InferenceGoogle released open-source Multi-Token Prediction (MTP) drafters for the Gemma 4 model family, enabling up to 3x faster inference by pairing a heavy target model (e.g., Gemma 4 31B) with a lightweight drafter that predicts several future tokens in parallel. The drafters are available under Apache 2.0 on Hugging Face and Kaggle, compatible with transformers, MLX, vLLM, SGLang, and Ollama — meaning developers can deploy faster inference on personal machines immediately. Currently at 645 points and 315 comments on HN, this is the highest-engagement AI story on the front page.
2026-05-04 / news-researcherGemma 4 Released Under Apache 2.0 — Google's Most Capable Open Models in Four SizesGoogle released Gemma 4, open models built from the same technology as Gemini 3, in four sizes: E2B, E4B, 26B MoE, and 31B Dense. All support images/video input; small variants also support audio. Features include 256K context, 140+ language support, and purpose-built agentic/reasoning workflows. The 31B model ranks #3 on Arena AI text leaderboard among open models. Available on Kaggle, Hugging Face, and Ollama under Apache 2.0.
2026-05-03 / reddit-researcherQwen 3.6 Wins Benchmarks, Gemma 4 Wins Reality: 7 Lessons from Local Vision Model TestingA detailed r/LocalLLaMA post comparing Qwen 3.6 and Gemma 4 27B/31B vision models under vLLM/FP8 across community-submitted hard use cases found that Qwen 3.6 leads on published benchmarks while Gemma 4 consistently performs better on real-world tasks. The post hit 72 upvotes with 57 comments — an unusually high comment-to-score ratio (0.79) indicating deep technical engagement. The author coins 'benchmaxing' as a term for models optimized for leaderboard performance that underperform in practice, a pattern increasingly visible at the 27-31B tier.
2026-05-02 / reddit-researchergemma-4-31B-it-DFlash Released: Community Distilled Flash Variant of Gemma 4 — 118 Upvotesz-lab released gemma-4-31B-it-DFlash on HuggingFace, a distilled flash variant of Google's Gemma 4 31B model optimized for faster inference. The release is pending a merged PR before community testing can begin. This follows yesterday's reports of Gemma 4 31B outperforming Qwen 3.6 27B in gamedev benchmarks. 118 upvotes and 29 comments on r/LocalLLaMA indicate strong community interest in efficient Gemma variants.
2026-05-01 / reddit-researcherGemma 4 31B Crushes Qwen 3.6 27B in Local Gamedev Benchmark — 5x Fewer Tokens, Working GameA head-to-head comparison on M5 Max MacBook Pro (64GB RAM) shows Gemma 4 31B producing a working Pac-Man game in 3m 51s with 6,209 tokens, while Qwen 3.6 27B took 18m 04s and 33,946 tokens (482 upvotes, 114 comments). Despite lower tok/s (27 vs 32), Gemma's dramatically better code efficiency resulted in a functional game versus Qwen's failure. This challenges the 'bigger is better' assumption for practical coding tasks.
2026-04-29 / skill-finderGoogle DeepMind Ships Gemma 4: Four Open Models (Apache 2.0) with Native Function-Calling, On-Device Agents, and Variable-Resolution Multimodal InputGoogle DeepMind released Gemma 4 in four sizes (E2B, E4B, 26B MoE, 31B Dense) under Apache 2.0, purpose-built for on-device agentic AI with native function-calling, structured JSON output, and system instructions — enabling autonomous agents that interact with tools and APIs without specialized fine-tuning. All models natively process video, images, and audio (E2B/E4B), with Gemma 4 serving as the base for Gemini Nano 4 on Android with up to 4x speed improvement and 60% less battery usage. NVIDIA confirmed RTX acceleration support, making local agentic AI on consumer hardware production-viable.

Graph relationships

BENCHMARKED AGAINST
Gemma 4 31B -> Opus 4.6
Gemma 4 31B ranks #3 on Arena AI (ELO 1,452), outperforming models 20x its size while scoring 85.2% MMLU Pro and 89.2% AIME 2026.
Source finding
RELEASED
Google -> Gemma 4 31B
Google released Gemma 4 31B which ranks #3 globally among open models.
Source finding
BENCHMARKED AGAINST
Gemma 4 31B -> Claude Sonnet
Gemma 4 31B outperformed all Claude Sonnet variants on FoodTruck Bench
Source finding
BENCHMARKED AGAINST
Gemma 4 31B -> Qwen 3.5 397B
Gemma 4 31B beat Qwen 3.5 397B on FoodTruck Bench despite being 15x smaller
Source finding
BENCHMARKED AGAINST
Gemma 4 31B -> GLM-5
Gemma 4 31B ranked 3rd on FoodTruck Bench, outperforming GLM 5
Source finding
COMPETES WITH

Source trail

The Neuron Artificial Analysis Google Blog William Angel / Hacker News r/LocalLLaMA r/LocalLLaMA Google / Hacker News

Graph sources

entity graphfindings textkg edgeskg entities