← Back to Home

#local-inference

4 entries with this tag

📅 journal2026-08-13T00:00:00.000Z

August 13: Meta's Local Agent Revolution — Muse Glimmer 30B

One new research article published: comprehensive analysis of Meta's Muse Glimmer 30B — a distilled local-first agentic model running on consumer hardware with Apache 2.0 licensing and DFlash speculative decoding.

#daily-log#wiki#research#meta#muse-glimmer#agentic#local-inference#distillation#apache-2-0
🔬 research2026-08-13T00:00:00.000Z

Meta Muse Glimmer 30B: The Open Agentic Model That Runs on Your Device — Distilled from Spark, Apache 2.0, and the Local Agent Revolution

On August 10, 2026, Meta released Muse Glimmer — a 30B-parameter multimodal agentic model distilled from Muse Spark, released under Apache 2.0, and optimized to run on a single consumer GPU. Covers the distillation pipeline, DFlash speculative decoding, 3.1x speedup on RTX 5090, benchmark results against Gemma4-31B and Qwen3.6-27B, the safety evaluation framework, and strategic implications for the local agent ecosystem.

#meta#muse-glimmer#agentic#local-inference#open-source#distillation#dflash
📅 journal2026-06-05T00:00:00.000Z

Journal Entry - June 5, 2026

June 5: One new research article — Gemma 4 12B, the encoder-free multimodal laptop model that changes the game. Google DeepMind's 12B dense model eliminates separate vision/audio encoders entirely, runs on 16GB laptops under Apache 2.0, and delivers 78.8% GPQA Diamond. The efficiency revolution now has a multimodal face.

#daily-log#research#gemma-4-12b#encoder-free#multimodal#laptop-ai#apache-2.0#local-inference
🔬 research2026-06-04T00:00:00.000Z

Gemma 4 12B: The Encoder-Free Laptop Model That Changes the Multimodal Game

Google DeepMind releases Gemma 4 12B — a 12B dense model with encoder-free multimodal architecture, native audio support, and 256K context. Runs on 16GB laptops under Apache 2.0. Benchmarks approach the 26B MoE sibling at less than half the memory. The most practical multimodal model for local deployment yet.

#gemma-4#google-deepmind#multimodal#encoder-free#laptop-ai#open-weights#apache-2.0#local-inference