Muse Glimmer-30B

--- title: “Muse Glimmer-30B” description: “Muse Glimmer-30B (Meta / MSL, August 9, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.” type: guide category: ai pubDate: 2026-08-12 image: /images/abs-model-muse-glimmer-30b.png imageAlt: “A retro robot representing Muse Glimmer-30B” imagePrompt: “Bold graphic editorial illustration, 1990s comic-book influence, heavy ink outlines, halftone texture, crimson and electric blue on cream. A single retro-futurist robot representing an AI model / a brain-in-a-server, no text, no logos, 16:9.” affiliate: false sources: - name: “Meta AI Research — Introducing Muse Glimmer, an open agentic model” url: “https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model” - name: “Hugging Face — Muse-Glimmer-30B model card” url: “https://huggingface.co/meta-models/Muse-Glimmer-30B” - name: “CNBC — Meta’s Muse Glimmer open-weight AI” url: “https://www.cnbc.com/2026/08/10/meta-muse-glimmer-open-weight-ai.html” - name: “VentureBeat — Meta returns to open source with Muse Glimmer” url: “https://venturebeat.com/technology/meta-returns-to-open-source-with-muse-glimmer-an-apache-2-0-licensed-30b-parameter-ai-model-optimized-for-agents-available-now” - name: “Hacker News — Muse Glimmer release thread (1,196 points)” url: “https://news.ycombinator.com/item?id=49241679” - name: “llm-stats.com — Muse Glimmer 30B comparison” url: “https://llm-stats.com/models/compare/granite-3.3-8b-instruct-vs-muse-glimmer-30b” - name: “ai.meta.com — Introducing Muse Spark and the Meta Model API” url: “https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/” - name: “Artificial Analysis — Muse Glimmer intelligence index” url: “https://artificialanalysis.ai/models/muse-glimmer” facts: - label: “Vendor” value: “Meta / MSL” - label: “Released” value: “August 9, 2026” - label: “License” value: “Open-weight (Apache 2.0)” - label: “Type” value: “AI model release” related: - abs-model-claude-opus-5 - abs-model-gpt-5.6-cyber - abs-model-grok-4.6 - abs-model-kimi-k3 - abs-model-lfm2.5-vl-3b - abs-model-ling-3.0-flash - abs-model-muse-spark-1.2 - abs-model-nemotron-3.5-lightning - abs-model-qwen-image-3.0 - abs-model-qwen3.7-flash - abs-model-qwen3.8-2.4t-a95b - abs-model-qwen3.8-max-weights-update - abs-model-solar-pro-4 tags: - muse-glimmer-30b - ai-models - model-release draft: false --- --- title: “Muse Glimmer-30B” description: “Muse Glimmer-30B (Meta / MSL, August 9, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.” type: guide category: ai pubDate: 2026-08-12 image: /images/abs-model-muse-glimmer-30b.png imageAlt: “A retro robot representing Muse Glimmer-30B” imagePrompt: “Bold graphic editorial illustration, 1990s comic-book influence, heavy ink outlines, halftone texture, crimson and electric blue on cream. A single retro-futurist robot representing an AI model / a brain-in-a-server, no text, no logos, 16:9.” affiliate: false sources: - name: “Meta AI Research — Introducing Muse Glimmer, an open agentic model” url: “https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model” - name: “Hugging Face — Muse-Glimmer-30B model card” url: “https://huggingface.co/meta-models/Muse-Glimmer-30B” - name: “CNBC — Meta’s Muse Glimmer open-weight AI” url: “https://www.cnbc.com/2026/08/10/meta-muse-glimmer-open-weight-ai.html” - name: “VentureBeat — Meta returns to open source with Muse Glimmer” url: “https://venturebeat.com/technology/meta-returns-to-open-source-with-muse-glimmer-an-apache-2-0-licensed-30b-parameter-ai-model-optimized-for-agents-available-now” - name: “Hacker News — Muse Glimmer release thread (1,196 points)” url: “https://news.ycombinator.com/item?id=49241679” - name: “llm-stats.com — Muse Glimmer 30B comparison” url: “https://llm-stats.com/models/compare/granite-3.3-8b-instruct-vs-muse-glimmer-30b” - name: “ai.meta.com — Introducing Muse Spark and the Meta Model API” url: “https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/” - name: “Artificial Analysis — Muse Glimmer intelligence index” url: “https://artificialanalysis.ai/models/muse-glimmer” facts: - label: “Vendor” value: “Meta / MSL” - label: “Released” value: “August 9, 2026” - label: “License” value: “Open-weight (Apache 2.0)” - label: “Type” value: “AI model release” related: - abs-model-claude-opus-5 - abs-model-gpt-5.6-cyber - abs-model-grok-4.6 - abs-model-kimi-k3 - abs-model-lfm2.5-vl-3b - abs-model-ling-3.0-flash - abs-model-muse-spark-1.2 - abs-model-nemotron-3.5-lightning - abs-model-qwen-image-3.0 - abs-model-qwen3.7-flash - abs-model-qwen3.8-2.4t-a95b - abs-model-qwen3.8-max-weights-update - abs-model-solar-pro-4 tags: - muse-glimmer-30b - ai-models - model-release draft: false --- ## Release Meta Superintelligence Labs shipped Muse Glimmer-30B on August 9, 2026 — the first fully open-weight model Meta has released since retiring the Llama family in April and going proprietary with the Muse Spark line. The weights are Apache 2.0, a deliberate step past the old Llama community license (which carried a 700M-MAU ceiling that blocked commercial deployment). Meta’s blog: “Today, we’re introducing Muse Glimmer, the next model from Meta Superintelligence Labs, and open sourcing the model weights under a permissive Apache 2.0 license.” (Meta AI Research). Zuckerberg, same day: “Today we’re also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we’ll also release the weights for Muse Spark 1.2.” (Hugging Face model card). The “soon” matters — Glimmer is the first shoe to drop in what looks like a staged re-opening of the Spark family. ## Why it matters Glimmer is the local half of a three-part post-Llama lineup, all sharing the Spark teacher lineage. Muse Spark is the closed frontier (cloud API). Muse Code is the closed terminal coding agent. Glimmer is the open-weights local agent — the model that runs on your machine. Per Meta’s blog, it was “trained on Muse Spark’s outputs using logit distillation,” so it inherits Spark’s behavior without Spark’s lock-in. Strategically, this is Meta’s answer to Chinese open-weight competition. Counterpoint’s Neil Shah in CNBC: “If Western tech giants only build walled gardens, developers and enterprise builders will naturally pivot to Chinese open-weight models… Meta can fill in this void well.” (CNBC). For operators, the practical point is concrete: Glimmer is the first 30B-class model that genuinely fits a 24 GB consumer GPU at full 131K context. r/LocalLLaMA and Hacker News field reports confirm a single RTX 3090 holds it with room to spare. ## Specs Glimmer is a dense causal transformer (not a mixture-of-experts model) — 29.6B total parameters (27.8B language + ~1.8B ViT-G/14 perception encoder). Architecture: - Layers: 52, hidden dim 6,656; SwiGLU FFN with intermediate dim 19,968. - Attention: hybrid — three local sliding-window (2,048) layers for every one global; 32 query heads / 2 KV heads (GQA 16:1); RoPE θ = 500,000 on local layers. - Context: 131,072 tokens. Text + image in, text out, across 100+ languages. Knowledge cutoff January 4, 2026. Vocabulary 202,048. - Quants: BF16 full precision, K-Quant-Dynamic (32 GB), K-Quant-17GB (24 GB). Quant degradation is small — ~0.2% and ~1.0% respectively. - Speedup: DFlash speculative decoding ships in the box — block-diffusion drafter emitting 16-token blocks, 3.1× on RTX 5090, 1.8× on M5 Max, 1.5× on M4 Max. Hardware target is a 24–32 GB VRAM envelope: RTX 3090, M4 Max, M5 Max. Local runtimes include Ollama, LM Studio, Unsloth, llama.cpp, MLX, ExecuTorch; server runtimes are vLLM and SGLang. ## Benchmarks Glimmer wins most agentic benchmarks in its size class against Gemma4-31B and Qwen3.6-27B. The gaps that matter for agent loops: - MCP Atlas: 75.5 vs 54.2 / 62.5 — a 13–21 point lead. - τ³-Banking: 23.5 vs 15.1 / 16.7. - DeepSearch QA: 74.6 vs 61.7 / 71.1. - WildClawBench: 47.6 vs 37.6 / 43.2. - Gaia2: 43.3 vs 36.4 / 40.0. - SWE-Bench Pro: 51.2 vs 36.9 / 50.2. - AIME 2026: 94.7 — ahead of both. - AA-LCR: 80.0 vs 68.3 / 73.3; Beam128K: 65.1 vs 58.2 / 63.0. Not a clean sweep — Qwen3.6-27B takes SWE-Bench Verified (77.2 vs 76.0), Gemma4-31B leads GPQA Diamond (85.7 vs 83.5), the two rivals split ScreenSpot Pro and MMMU Pro by a point. The Artificial Analysis Intelligence Index scores it 35; it tops the Hugging Face Open Leaderboard sub-32B tier on several tasks. (Artificial Analysis). Meta’s Chief AI Officer Alexandr Wang summarized the design intent: “just like much larger models, muse glimmer can operate as a fully capable agent via planning, tool calls, checking its own results, and failure recovery” and “can run on 24GB of VRAM without losing agentic reliability.” (VentureBeat). ## Strengths 1. Best agentic tool-use in its class. MCP-Atlas, τ³-Banking, SWE-Bench Pro, WildClawBench, DeepSearch QA, AA-LCR — exactly the benchmarks that decide whether an agent drives tools. 2. Truly runs locally. Apache 2.0, ~17–20 GB after quantization, fits a 24 GB GPU or M-series MacBook with full 131K context on-device. 3. DFlash speculative decoding ships in the box. 3.1× throughput on RTX-class hardware with no separate integration. 4. Multimodal perception built in via the 1.8B ViT-G/14 encoder — screenshots, charts, documents without bolting on a CLIP stack. 5. Permissive license. Apache 2.0 with no MAU cap. VentureBeat: “Meta is treating the developer workstation as a credible deployment target for autonomous agents, rather than merely a place to experiment with smaller language models.” 6. Strong reasoning at AIME 2026 (94.7) despite being a distillate. ## Weaknesses 1. Marginal gain over Qwen3.6 on non-agentic work. Dominant Hacker News critique: Glimmer only “barely edges out” Qwen3.6, and risks being overtaken by an imminent Qwen3.8-27B. 2. Slow even with DFlash on Macs. Field reports cite ~60 tok/s on a 5090 with the drafter, but community consensus is “go walk outside” speed on M-series laptops. Fluid real-time talk is hardware-dependent. 3. No Meta-hosted API. Forces every operator onto partner hosting (Together, Fireworks, OpenRouter) or self-host. 4. Input is text + image only, despite some third-party listings listing audio/video. The official card says text + image in, text out. 5. Closed training data and recipe. VentureBeat: “it is the weights that are open — Meta has not released the training data or training code.” No fine-tuning from scratch, no dataset bias audit. 6. Sits below Muse Spark on the frontier. Meta’s own risk framework rates Glimmer lower-risk partly because it is weaker than the closed model — so for genuinely hard tasks you still need Spark. ## Operator call For an operator who needs a default local agent today, Glimmer is the strongest 30B-class option on the market as of August 2026. The 30B / 24 GB envelope is the sweet spot for always-on agent loops with tool-use, MCP support, and failure recovery without burning cloud tokens — and Glimmer beats Qwen3.6-27B

Sources

• Meta AI Research — Introducing Muse Glimmer, an open agentic model
• Hugging Face — Muse-Glimmer-30B model card
• CNBC — Meta’s Muse Glimmer open-weight AI
• VentureBeat — Meta returns to open source with Muse Glimmer
• Hacker News — Muse Glimmer release thread (1,196 points)
• llm-stats.com — Muse Glimmer 30B comparison
• ai.meta.com — Introducing Muse Spark and the Meta Model API
• Artificial Analysis — Muse Glimmer intelligence index