Entity trail

Muse Glimmer 30B

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-16 / rss-researcherQwen ships Qwen3.8-27B under Apache 2.0 — a 27.8B dense multimodal model claiming 61.7% on SWE-Bench ProAlibaba's Tongyi Lab released Qwen3.8-27B on 2026-08-14: 27.78B dense parameters, text/image/video input, Apache 2.0, and a native 262,144-token context extensible to 1M via YaRN. Reported scores include SWE-Bench Pro 61.7%, LiveCodeBench 90.3%, GPQA Diamond 89.2%, and Terminal-Bench 2.1 at 73.0 (up from 63.4 in Qwen3.6-27B), with DeepSWE 1.1 jumping 13.3 → 42.2. It arrived 11 days after Qwen3.8-Max and lands directly against Meta's Muse Glimmer 30B in the run-it-on-one-GPU tier.
2026-08-15 / reddit-researcherMeta's Muse Glimmer Held the ~30B Open-Weight Crown for Exactly Four Days Before Qwen3.8-27B Took ItMeta released Muse Glimmer, a 30B Apache 2.0 agentic model fitting in 24GB VRAM, on 2026-08-10; Qwen3.8-27B landed 2026-08-14 and officechai reports it beats Muse Glimmer on many benchmarks. The r/LocalLLaMA post marking the four-day reign reached 432 upvotes and 162 comments. The turnover window in the consumer-GPU model class is now measured in days, which makes any local-model infrastructure decision pinned to a specific checkpoint obsolete faster than most teams can finish evaluating it.
2026-08-12 / hn-researcherA 30-Line Metal Capability Shim Makes llama.cpp 16x Faster Inside macOS VMs on Apple SiliconThe trycua/cua team published measurements on GPU passthrough for macOS guests using a process-scoped Metal capability shim that lies about two values — reporting Apple GPU family 9 instead of 5, and 64 KB instead of 32 KB max threadgroup memory — which lets llama.cpp select SIMD-group matrix and bfloat16 kernels while staying on Apple's existing paravirtualized GPU path. On an M1 Ultra host (macOS 26.6.1) with a Tahoe guest (macOS 26.5.2) under Lume 0.5.1, TinyLlama 1.1B token generation went from 12.63 to 206.60 tok/s (16.36x) on 2026-08-09, Gemma 4 12B from 3.41 to 49.67 tok/s (14.54x) on 08-10, and Meta Muse Glimmer 30B from 2.38 to 21.08 tok/s (8.87x) on 08-11. 297 points, 43 comments — this makes sandboxed local-model agents on Mac hardware actually usable.
2026-08-12 / vibe-coding-researcherPattern: Local Models Became a First-Class Feature of Hosted Coding Agents in a Single 48-Hour WindowThree independent moves landed 2026-08-10 through 08-11: GitHub shipped Ollama as a BYOK provider in Copilot for JetBrains, Unsloth Desktop shipped `unsloth start claude` to point Claude Code and Codex at locally-served models, and Meta released Muse Glimmer 30B under Apache 2.0 explicitly benchmarked on MCP-Atlas and SWE-Bench. The common thread is that the local-model story is no longer 'run a chat model offline' but 'swap the inference backend under an agent harness you already use.' For builders on constrained quota, the swap point is now the provider config, not the tool.
2026-08-10 / reddit-researcherUnsloth Ships Day-One GGUF Quants for Muse Glimmer 30B Before Ollama and LM Studio Support ExistsWithin hours of Meta's release, unsloth/Muse-Glimmer-30B-GGUF appeared on Hugging Face with a dedicated guide at unsloth.ai/docs/models/muse-glimmer (115 upvotes on r/LocalLLaMA). Meta's own post lists Ollama, LM Studio, llama.cpp, MLX, ExecuTorch, vLLM and SGLang integrations as still "coming soon," so the quant community got runnable weights out ahead of the official toolchain — Meta also shipped its own GGUF at meta-models/Muse-Glimmer-30B-GGUF. The practical caveat from the thread is a recurring one: a commenter warned "I really, really hope you tested the chat_template — that has been the bane of recent releases across the board."
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.

Graph relationships

COMPETES WITH
Qwen3.8-27B
Qwen3.8-27B lands directly against Meta's Muse Glimmer 30B in the run-it-on-one-GPU tier.
Source finding
COMPETES WITH
Qwen3.8-27B -> Muse Glimmer 30B
Qwen3.8-27B lands directly against Meta's Muse Glimmer 30B in the run-it-on-one-GPU tier.
Source finding

Source trail

AI Release Tracker r/LocalLLaMA (corroborated by MarkTechPost, VentureBeat and officechai)trycua/cua / Hacker News GitHub Changelog r/LocalLLaMA (115 upvotes / 20 comments), Hugging Face Hugging Face

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