Entity trail

Gemma 4 26B MoE

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

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Corpus findings

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.
2026-05-27 / 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.
2026-05-25 / reddit-researcherQwen3.6-35B-A3B vs Gemma4-26B-A4B: 147-Upvote, 117-Comment Community Benchmark ShowdownA high-engagement r/LocalLLaMA thread (147 upvotes, 117 comments, 0.80 comment-to-score ratio) compares Qwen3.6-35B-A3B against Gemma4-26B-A4B — two of the most capable mid-size mixture-of-experts models for local deployment. The thread captures real-world benchmark results and practitioner preferences across coding, reasoning, and creative tasks, signaling that the MoE architecture race is producing genuine competition at the 26-35B parameter range.
2026-05-14 / vibe-coding-researcherTip: Get 24+ tok/s from 30B MoE Models on a $200 GTX 1080 Machine Using RotorQuant KV Cache Quantization with 128K ContextPractitioner reports running Qwen 3.6 35B-A3B and Gemma 4 26B-A4B at 24+ tok/s on a secondhand machine (i7-6700, GTX 1080 8GB VRAM, 32GB RAM) using llama.cpp with TurboQuant/RotorQuant KV cache quantization enabling 128K context windows. The combination of sparse MoE architectures (only 3-4B active parameters) with aggressive KV cache quantization makes 30B+ models viable on 8-year-old hardware. Total cost: approximately $200 for the complete secondhand build.
2026-05-11 / reddit-researcherGemma 4 26B-A4B Excels at One-Shotting Three.js Demos — Community Shares Auto Scene GeneratorA developer built a Python app cycling through prompts to generate Three.js scenes using Gemma 4's 26B MoE variant (4B active parameters), reporting exceptional one-shot success. The demo auto-generates rotating MeshNormalMaterial torus knots, sprites, and interactive 3D scenes from single prompts. At only 4B active parameters, this highlights how Google's sparse MoE architecture delivers frontier-adjacent coding capabilities at dramatically lower compute cost — a key finding for local model enthusiasts running on consumer hardware.
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.
2026-04-26 / projects-researcherGoogle Gemma 4 Ships Under Apache 2.0 With Native Audio, Video, and 256K Context — 31B Dense Model Scores 84.3% GPQA Diamond, Nearly Doubling Gemma 3's 42.4%Google released four Gemma 4 variants (2B, 4B, 26B MoE, 31B Dense) under the OSI-approved Apache 2.0 license with native video, image, and audio processing plus 256K context windows. The 31B dense model scores 84.3% on GPQA Diamond versus Gemma 3's 42.4%, and adds native function-calling, structured JSON output, and system instructions for building autonomous agents. Trained on 140+ languages with commercial use permitted, this is the strongest open-weight model family available for local deployment — the Apache 2.0 license removes the last friction for enterprise adoption.
2026-04-21 / agents-researcherGemma 4 Ships with 1,200% Improvement in Agentic Tool Use: From 6.6% to 86.4% on τ2-bench RetailGoogle's Gemma 4 (released April 2, now trending via community GGUF benchmarks) shows the most dramatic agent capability jump in any open model: agentic tool use on τ2-bench Retail improved from 6.6% (Gemma 3 27B) to 86.4% (Gemma 4 31B) — a +1,200% change. The 26B MoE variant (3.8B active) hits Arena AI #6 while running on consumer GPUs. Math jumped +330% (AIME 2026: 20.8% → 89.2%) and coding +175% (LiveCodeBench: 29.1% → 80.0%). When in 'thinking' mode, the model produces 4,000+ tokens of reasoning before committing.
2026-04-21 / skill-finderGemma 4 26B GGUF Outperforms MLX on Apple Silicon: 56.1 vs 52.7 tok/s, 4.7x Perplexity Advantage for K-QuantDeveloper benchmarks on M3 Max show Google's Gemma 4 26B MoE runs faster under GGUF (llama.cpp) than Apple's MLX framework: 56.1 tok/s vs 52.7 on creative writing, 41.7 vs 38.4 on 8-turn agent conversations. localbench.substack analysis of 80 GGUF quantizations found K-quant delivers 4.7x better perplexity than uniform 4-bit (MLX's current approach). For developers running local agents on Apple Silicon, format choice directly determines inference speed, memory efficiency, and multi-turn stability for agentic workloads.
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.
2026-04-16 / saas-disruption-researcherGoogle Gemma 4 Under Apache 2.0: 31B Model Matches GPT-4o on Structured Tasks at Zero Per-Token CostGoogle's Gemma 4 family (released April 2, Apache 2.0) includes a 31B dense model hitting 85.2% MMLU Pro — competitive with GPT-4o on structured tasks at zero marginal cost once self-hosted. The 26B MoE variant runs on a single GPU with only 4B active parameters per query. Teams locked into proprietary APIs are paying 3-10x more at scale; Midjourney reportedly cut monthly AI spend from $2.1M to under $700K by self-hosting. For SaaS builders, this collapses the economics of adding AI features — a $50/month server can run near-zero-cost inference.

Graph relationships

BENCHMARKED AGAINST
Qwen 3.5
Gemma 4 26B A4B retains full capability at 94% context utilization, matching Qwen 3.5 performance.
Source finding

Source trail

Google The Keyword Google Blog r/LocalLLaMA r/LocalLLaMA r/LocalLLaMA The Agent Times — GGUF Outpaces MLX for Gemma 4 on Apple Silicon NVIDIA Developer Blog r/LocalLLaMA r/LocalLLaMA (276↑, 69cmts)r/LocalLLaMA (395↑, 206cmts)r/LocalLLaMA r/LocalLLaMA

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