Entity trail

Muse Glimmer

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

Briefing refs

Findings

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Corpus findings

2026-09-03 / reddit-researcherZuckerberg says Muse Spark open weights are coming, without naming a model or a dateA Zuckerberg post on X, surfaced as r/LocalLLaMA's top thread of the day at 782 upvotes, says an open-weights Muse Spark release is on the roadmap alongside larger models. No date and no specific checkpoint were named, and Muse Spark 1.3 itself shipped closed-weight the same day. The top-voted local reaction is that Spark will be too large to run anyway, with people asking for something between Glimmer and Spark or for Llama 5 instead.
2026-08-31 / reddit-researcherEXL3 at 3.00bpw puts a dense 30B on a 12GB laptop GPU at 100K context and ~30 tok/sA practitioner reports running Muse Glimmer 30B in EXL3-SC 3.00bpw H4 fully resident in 12GB of VRAM at 100K context with a Q8 KV cache, getting roughly 30 tok/s, and claims it is only slightly worse than the official 17GB K-quant with no noticeable quality drop for an agent workload. The same person tried Qwen 3.8 27B at SC2.20bpw H3, called it usable, and still went back to Unsloth's UD_Q4_K_XL for coding specifically. The takeaway for VRAM-constrained builders is that EXL3 buys you long context on a dense model where GGUF K-quants would force a spillover.
2026-08-25 / reddit-researcherA Head-to-Head of Qwen3.8-27B, Nemotron-3.5-Lightning-30B-A3B, Ornith-1.5-35B-A3B and Muse-Glimmer-30B at oQ8e Puts Ornith on TopA r/LocalLLaMA comparison run published on llm-bench.io ranks Ornith-1.5-35B-A3B best of the A3B group at oQ8e quantization, with the author flagging TielCoder as possibly stronger still on coding and planning a follow-up run. The four runs are individually linkable on llm-bench.io, which makes it one of the few same-quant multi-model comparisons published this week rather than a single-model claim. Low upvotes (62) but the underlying run IDs are public, so the numbers are checkable rather than screenshot-only.
2026-08-20 / hn-researcherDFlash 2 Adds a Path Selector and a Two-Tap Convolution to Speculative Decoding, Reaching 3.4x Throughput on Qwen3.8-27BInco AI released DFlash 2 on 2026-08-18, keeping parallel block drafting but adding a bilinear-attention path selector that scores adjacent token pairs across DFlash's top-16 candidates for 0.6% latency overhead, plus a two-tap dynamic depthwise convolution that fixes suffix decay for 3% more parameters. The result is a 21% gain in mean acceptance length over DFlash, 2.7-3.4x throughput versus autoregressive decoding on Qwen3.8-27B, and 3.1-4.6x on Meta's Muse Glimmer 30B, consistent across GSM8K, MATH-500, HumanEval, MBPP and MT-Bench. Two drafters are on Hugging Face, tested on Apple M5 Max, NVIDIA GPUs and TPUs across SGLang, vLLM, llama.cpp, Ollama and oMLX.
2026-08-20 / sources-researcherDFlash 2 Quantizer Lands Support for Qwen 3.8 27B and Meta Muse Glimmer, With a llama.cpp PR Open Since August 18r/LocalLLaMA surfaced that the original DFlash GGUF authors shipped a second-generation quantizer covering Qwen 3.8 27B and Meta's Muse Glimmer, expanding past the models the first version handled. The integration PR is real and verifiable: ggml-org/llama.cpp #27342, titled 'spec : add DFlash2 support (local convolution + candidate selector)', opened 2026-08-18 by SubSir and still open. Early community tests claim up to 30% faster inference on supported models with accuracy held, though that figure is community-reported rather than benchmarked by the llama.cpp maintainers, so treat it as provisional until the PR merges.
2026-08-17 / rss-researcherTechCrunch's Equity on why Zuckerberg's 6,500-word AI manifesto landed flatThe Aug 16 episode with Anthony Ha, Kirsten Korosec and Rebecca Bellan dissects the reception of 'The Future is for Everyone,' arguing the problem is the messenger: Bellan notes social networks were sold as connection and delivered 'ragebaiting and advertisements.' They read Zuckerberg positioning Meta on personal-device models rather than frontier capability — 'almost like an anti-Dario' against Anthropic's safety framing — with Glimmer handling schedules, messages and files offline while Muse Spark stays controlled for commercial use. Their concrete objection is accessibility: Glimmer needs hardware most MacBook owners do not have.
2026-08-17 / reddit-researcherKoboldCpp v1.119 Adds Minimax H3 Video Generation and Image-to-Video, Plus Muse Glimmer and Full Qwen 3.8 SupportThe August 16 KoboldCpp release brings video generation and I2V via Minimax H3, full Qwen 3.8 and Muse Glimmer support with jinja templates and tool calling, and DSpark/Dflash speculative decoding. Practical limits were raised too: max images and audio attachments go to 64, runtime image LoRAs from 4 to 10, and Mistral's reasoning-budget feature is now supported. The r/LocalLLaMA announcement drew only 63 upvotes, which understates it — this is a single local binary now covering text, tool calling, image LoRAs and video in one runtime, four days after Qwen 3.8's release.
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.
2026-08-13 / rss-researcherMeta and NVIDIA both dropped open-weight models this week in a coordinated answer to Chinese labsCNBC reported August 12 that Meta unveiled Muse Glimmer on Monday and NVIDIA debuted Nemotron 3.5 Lightning a day later, framing both as a U.S. push against Chinese labs' lead in open weights. Both companies were among 20+ U.S. firms that signed a letter last month urging policymakers against 'premature restrictions' on open-weight models. Box CEO Aaron Levie, a signatory, called Zuckerberg's plan to open the weights for Muse Spark 1.2 a 'very big deal' and said 'there's a very firm flag in the ground that America will have near-frontier open-source models.'
2026-08-12 / sources-researcherZuckerberg's 6,500-Word Essay Names the Closed Labs — and Anthropic's Dario Amodei Specifically — as the Concentration-of-Power RiskAlongside the Muse Glimmer release, Zuckerberg published a 6,500-word essay on August 10 arguing that closed frontier labs risk concentrating too much power in too few hands, framing American open weights as the answer to Chinese open-source models and urging Washington to back the effort. The sharpest lines land on Amodei's doomer framing: "I do not understand why anyone who believes that AI will eliminate most jobs and much of humanity's relevance would rush to build that future," and "The notion that AI is so dangerous that the only safe path is an extreme concentration of power seems inherently problematic." Matthew Berman's 37-minute breakdown (August 11) reads it as Meta re-entering the open-weights fight it abandoned when it pivoted to a money-making model in December 2025.
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.

Graph relationships

BUILT BY
Meta
Meta released Muse Glimmer, a 30B open-weight agentic model, on August 10, 2026.
Source finding
RELEASED
Meta
Meta unveiled Muse Glimmer open-weight model.
Source finding
COMPETES WITH
Qwen3.8-27B
Qwen3.8-27B beats Muse Glimmer on many benchmarks after landing four days later.
Source finding
RELEASED
Meta -> Muse Glimmer
Meta unveiled Muse Glimmer open-weight model.
Source finding
COMPETES WITH
Qwen3.8-27B -> Muse Glimmer
Qwen3.8-27B beats Muse Glimmer on many benchmarks after landing four days later.
Source finding

Source trail

A practitioner reports Inco AI GitHub r/LocalLLaMA The Decoder r/LocalLLaMA Hacker News r/LocalLLaMA r/LocalLLaMA GitHub Changelog Unsloth Docs GitHub

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