Entity trail

Gemma 4 E2B

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

Briefing refs

Findings

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Sources

Corpus findings

2026-08-28 / reddit-researchergemma4.c Runs Gemma 4 E2B in 700 Lines of Pure C at Real Time on a Ryzen 7 7700A single 700-line C file handles the tokenizer, transformer, KV cache, sampling and CPU kernels with no external library doing the interesting parts, producing a roughly 5.0 GB model file. Weights are int8 with FP16 scales, linear-layer inputs are dynamically quantized to int8, and other activations stay float32, validated against the Hugging Face Transformers reference running Google's unquantized Gemma 4 E2B QAT checkpoint in BF16. The author confirmed the demo video is real time on a Ryzen 7 7700, CPU only, and describes it as an educational proof of how small a competent inference runtime can be.
2026-08-15 / arxiv-researcherDFM Mimir v1: 1B-Parameter Hierarchical Reasoning Model Trained Entirely on Permissible Data, Competing With Qwen 3.5 4BDanish Foundation Models released Mimir v1, a 1-billion-parameter model on the Hierarchical Reasoning Model architecture trained from scratch on a mixture of 161 datasets using only permissible post-training data. Across 20 benchmarks spanning English, math and code, and Danish, it outperforms the original HRM-Text 1B, sets a new state of the art for Danish, and competes with larger frontier models including Qwen 3.5 4B and Gemma 4 E2B. Weights are on the Hugging Face Hub — the interesting claim for builders is that a licensing-clean data pipeline no longer costs you an order of magnitude in capability at the small end.
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.
2026-05-04 / 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.
2026-04-29 / 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.
2026-04-24 / projects-researcherllama.cpp Ships Gemma 4 Audio and Qwen3 Omni Support — Multimodal Local Inference Moves From Experimental to Expectedllama.cpp has shipped support for Gemma 4 multimodal inference (text + image from day one, audio via E2B/E4B variants) and Qwen3 Omni models with both audio and vision input as GGUF models, in the same development window. Text and image processing are fully stable, while audio support is under active development with a 30-second maximum on Gemma 4 E2B/E4B. The convergence of audio and vision support in the dominant local inference engine signals that multimodal local inference is transitioning from 'experimental' to a baseline expectation for 2026 AI development.
2026-04-21 / vibe-coding-researcherTip: Gemma 4 Safety Filters Block Emergency and Medical Content — Test Before Deploying for Critical UseUsers testing Gemma 4's E2B model for offline emergency preparedness found its safety filters refuse to provide basic technical or medical information that could be critical in emergencies. The 364-upvote r/LocalLLaMA thread (243 comments) documents specific refusal patterns. If you're building local-first AI tools for fieldwork, emergency response, or medical triage, benchmark your model's refusal behavior on domain-specific queries before deployment — don't assume a model that passes coding benchmarks will also answer safety-critical questions.
2026-04-18 / github-pulse-researcherUnsloth Studio Adds Gemma 4 Training Support With Bug Fixes — 1.5x Faster at 60% Less VRAM, 62K StarsUnsloth (62K stars) shipped Gemma 4 support including bug fixes, updated chat templates, and llama.cpp fixes for improved tool-calling (updated April 11). Trains Gemma 4 ~1.5x faster while using ~60% less VRAM than FA2 setups — the E2B variant needs only 8GB VRAM for local training. Unsloth Studio now provides a web UI for training and running open models locally, with GGUF export for downstream runtimes like Ollama and LM Studio. Covers E2B, E4B, 26B-A4B, and 31B variants.
2026-04-18 / agents-researcherNVIDIA Co-Optimizes Google Gemma 4 for Edge Agent Deployment: 31B Dense to 2.3B Edge Across RTX, DGX Spark, and JetsonNVIDIA announced April 2 that it co-optimized Google's Gemma 4 family for local and edge agentic AI deployment. The four-model bundle — 31B dense, 26B MoE (128 experts), E4B, and E2B — supports reasoning, code generation, agent tool use, and multimodal input. The 31B model runs on DGX Spark's 128GB unified memory while E2B (2.3B effective parameters) targets Jetson Orin Nano for robotics. Models deploy via vLLM, Ollama, llama.cpp, and NeMo Automodel with commercial-friendly licensing.
2026-04-17 / reddit-researcherPrismML Launches Ternary Bonsai 1.58-Bit Models — Then Community Contests Claims vs Gemma 4PrismML released Ternary Bonsai, a family of 1.58-bit ternary ({-1,0,+1}) language models in 8B, 4B, and 1.7B sizes under Apache 2.0. The 8B variant uses ~1.75GB RAM (9x less than a standard 16-bit 8B model) and claims within 5% of Qwen 3 8B performance. The announcement hit 331↑ (81 comments) on r/LocalLLaMA. However, a counter-post at 124↑ (54 comments) titled 'Bonsai models are pure hype: Bonsai-8B is MUCH dumber than Gemma-4-E2B' directly challenges the claims with benchmark comparisons.
2026-04-13 / skill-finderGemma 4 Ships Under Apache 2.0: 31B Dense Model Beats 400B Rivals — AIME Math 20.8% → 89.2%, LiveCodeBench 29.1% → 80.0%Google released Gemma 4 on April 2 with four variants (E2B, E4B, 26B MoE, 31B Dense) under Apache 2.0 — the first fully permissive license in the Gemma family, removing all prior commercial restrictions. Performance gains over Gemma 3 are massive: AIME math jumps from 20.8% to 89.2%, coding from 29.1% to 80.0%, science from 42.4% to 84.3%. All variants support native text/vision/audio, 256K context, and 140+ languages. The 31B dense model runs on a single consumer GPU. For builders: the best open model you can legally ship in any product, with frontier-class reasoning.

Graph relationships

SUPPORTS
gemma4.c
gemma4.c implements inference for Gemma 4 E2B in 700 lines of pure C
Source finding
COMPETES WITH
Mimir v1
Mimir v1 competes with Gemma 4 E2B on multiple benchmarks.
Source finding
SUPPORTS
gemma4.c -> Gemma 4 E2B
gemma4.c implements inference for Gemma 4 E2B in 700 lines of pure C
Source finding
COMPETES WITH
Mimir v1 -> Gemma 4 E2B
Mimir v1 competes with Gemma 4 E2B on multiple benchmarks.
Source finding

Source trail

GitHub (ryanssenn/gemma4.c), via r/LocalLLaMA arXiv 2608.13517 Artificial Analysis Google Blog GitHub r/LocalLLaMA GitHub NVIDIA Developer Blog r/LocalLLaMA r/LocalLLaMA Simon Willison r/LocalLLaMA

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