Entity trail

Gemma4

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

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Findings

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Corpus findings

2026-09-02 / reddit-researcherSpark-X2.5 arrives as a genuinely new 4B/1.7B architecture on Apache 2.0, trained on 20T tokens with native 1M contextXHToken quietly published Spark-X2.5-4B and Spark-X2.5-1.7B on Hugging Face. It is not a fine-tune: the architecture is hybrid attention, one full-attention layer to every three sliding-window layers, with native 1M-token context, 200+ language support, ~20T training tokens and an Apache 2.0 license. Claimed scores for the 4B include 65.1 on BFCL-V4, 75.1 on tau-squared-bench, 44.4 on SWE-Bench Pro and 90.7 on AIME 2026, benchmarked against Qwen3.5 and Gemma4 at 2B through 12B. It does not run on upstream llama.cpp yet — PR ggml-org/llama.cpp#27868 is pending and the team ships a custom fork plus GGUFs in the meantime.
2026-08-30 / arxiv-researcherBekchiAI Releases 2,057 Verifier-Checkable Agent Tasks Where Gold Answers Are Computed, Not WrittenBekchiAI pairs a benchmark of 13 tool-using ReAct agents across 7 categories (arithmetic, structured/SQL, security detection, URL grounding, planning, orchestration, tool-policy) totalling 2,057 deterministic committed tasks with a web observability and control layer for live agents. Every gold answer is computed by running canonical SQL against a real database, computing an exact DAG schedule, or evaluating closed-form lambdas, and adversarial security samples are deliberately paired with imperfect signature scanners so a score reflects the model's own judgment rather than copying an oracle. It reports tool-call adherence, URL hallucination and source-match, and per-model token cost across Qwen3.7-Max, gemma-4-31B-it, gemma4:26b and gpt-oss-120b, with benchmark, evaluation tools and platform publicly released.
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.
2026-08-11 / github-pulse-researcherclaudish-to-english Runs a Local 26B Model Purely to Rewrite Claude's Prose Into Plain English Before You Read Itgvzdv/claudish-to-english appeared 2026-08-10 and took 256 stars overnight. It's a Claude Code plugin using a `MessageDisplay` hook to rewrite assistant responses through a local ollama model as they stream, plus an opt-in `PostToolUse` hook for Markdown files. Defaults: `gemma4:26b-mlx` (~17 GB), ~60 tokens/second, a 200-character minimum before it engages, 45s display timeout, 150s Markdown timeout, and fail-open behavior showing the original text if ollama is unreachable. Display-only — transcripts and the model's own reasoning keep the original text. That someone will spend 17 GB of local VRAM to avoid reading model-flavored prose is a real, quantified data point on how much the house style now costs.
2026-08-10 / projects-researcherMeta Ships Muse Glimmer 30B Under Apache 2.0 — a 29.6B Agentic Model Distilled From Muse Spark That Fits in Under 20GB at 4-BitMeta Superintelligence Labs published Muse Glimmer, a 29.6B dense causal transformer (52 layers, 6,656 hidden dim) with a ~1.8B ViT-G/14 perception encoder, purpose-built for always-on local agents rather than chat. It ships Apache 2.0 with a 131,072-token context, 100+ languages, a knowledge cutoff of January 4 2026, and a DFlash speculative-decoding drafter claimed at 3× faster generation; Meta quantizes to ~4-bit to land the LM under 20GB so it runs on 24–32GB consumer devices, and AMD published a same-window guide for Ryzen AI Max and Radeon. Reported SWE-Bench Pro is 51.2%, positioned against Gemma4-31B and Qwen3.6-27B. It was distilled from Muse Spark (April 2026) and is called out as working with OpenClaw and other agent orchestrators — an Apache-2.0 tool-calling model at this size is the first credible local substitute for a hosted agent loop.
2026-07-26 / hn-researcherShow HN: OpenLake Offloads KV Cache to a Shared Petabyte Pool, Claiming 66× Faster Time-to-First-TokenOpenLake, a Rust/io_uring storage engine posted to Show HN today, moves LLM KV cache off the GPU into a petabyte-scale store co-located on GPU hosts so a prefix computed on one GPU can be served to others, claiming million-plus IOPS within 1ms and a 66× time-to-first-token speedup on cache hits at 128K context. The demo serves Gemma4-31B on an H100 at 256K context, and it plugs into vLLM through an `openlake-vllm` connector configured via `--kv-transfer-config`. Apache-2.0, 2.3K stars, 423 forks, 164 commits, with 91 open issues — early but the most concretely useful long-context cost lever posted this week.
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.
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.
2026-05-24 / reddit-researcherRun Chrome's Gemma4 (Gemini Nano) Directly on PC Without GPU — Step-by-Step TutorialA tutorial on r/LocalLLaMA (73 upvotes, 34 comments) shows how to extract and run Chrome's built-in Gemma4 model (marketed as Gemini Nano) directly on any PC without a GPU. The guide emerged after the community discovered Chrome secretly downloads the model. While the model is small and limited, the tutorial demonstrates that browser-embedded AI models can be repurposed for standalone local inference, lowering the barrier to on-device AI to essentially zero additional setup.
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 / vibe-coding-researcherPattern: Multi-Token Prediction Merging Into Every Inference Stack as Standard OptimizationMTP is no longer experimental — it's infrastructure. llama.cpp merged native support (May 16), vLLM already supports it via Gemma4MTPModel, and four major model families ship MTP heads trained from scratch (DeepSeek V3/V4, Qwen 3.6, Gemma 4). The pattern: MTP is following the trajectory of flash attention — from research novelty to expected default. For coding agent builders running local inference, MTP eliminates the need for separate draft models while delivering better acceptance rates (70–85% vs ~50%). Expect Ollama and other runtimes to add MTP support within weeks.
2026-05-11 / reddit-researcherExLlamaV3 Major Updates — turboderp on 'Absolute Tear' of Commits for Local InferenceThe r/LocalLLaMA community highlighted turboderp's rapid-fire development on ExLlamaV3, the optimized quantization and inference library for running LLMs on consumer GPUs. Recent releases include v0.0.29 with Gemma4 support, Python 3.14 wheels, Torch 2.11 wheels, loop detection, and segfault fixes for large tensor saves. The dev branch shows continuous commits pushing quantization boundaries. ExLlamaV3 remains the go-to backend for local inference via TabbyAPI, with the community tracking each update closely.

Graph relationships

BENCHMARKED AGAINST
Spark-X2.5-4B
Spark-X2.5-4B was benchmarked against Gemma4 across 2B through 12B model sizes.
Source finding
COMPETES WITH
Qwen3.6
Qwen3.6 is emerging as the top local model for agentic use, competing with Gemma4 and Llama variants.
Source finding
BENCHMARKED AGAINST
Spark-X2.5-4B -> Gemma4
Spark-X2.5-4B was benchmarked against Gemma4 across 2B through 12B model sizes.
Source finding
COMPETES WITH
Qwen3.6 -> Gemma4
Qwen3.6 is emerging as the top local model for agentic use, competing with Gemma4 and Llama variants.
Source finding

Source trail

Muse Glimmer unsloth/Muse-Glimmer-30B-GGUF Meta's technical blog posted on X independent public harness GitHub Hugging Face (via r/LocalLLaMA, 201 upvotes)arXiv 2608.26867 GitHub (ryanssenn/gemma4.c), via r/LocalLLaMA GitHub r/LocalLLaMA r/LocalLLaMA

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