Meta Muse Glimmer
by Meta (Meta Superintelligence Labs)
Apache 2.0 open-weight 30B model built to run always-on AI agents on a single consumer GPU
Muse Glimmer is Meta's 30B open-weight, Apache 2.0-licensed model purpose-built for always-on local AI agents. It handles multi-step tool use, long task trajectories and failure recovery, ships with an integrated vision encoder and 4-bit builds that fit a single 24GB consumer GPU, and is aimed at platform and engineering teams that want agent workloads running on their own hardware rather than a metered cloud API.
Muse Glimmer is a 29.6-billion-parameter dense transformer released by Meta Superintelligence Labs on 10 August 2026 under an Apache 2.0 licence — the most permissive terms Meta has applied to a model of this class, and a deliberate break from the restrictive Llama Community Licence that complicated commercial deployment. It is engineered for one job: running always-on agents locally. The architecture comprises 52 transformer layers, grouped-query attention with 32 query and 2 key-value heads, an interleaved attention pattern of three local layers (2,048-token sliding window) to one global layer, a 202,048-token vocabulary, a context window of 131,072-plus tokens, a knowledge cutoff of 4 January 2026, and an integrated ~1.8B-parameter ViT-G/14 vision encoder accepting up to 4,096 visual tokens per image, making it multimodal rather than text-only. Meta trained it by logit distillation from its proprietary Muse Spark model, then mid-trained on agent-heavy data with reasoning traces, followed by supervised fine-tuning, on-policy distillation and reinforcement learning across general, reasoning, coding and agentic domains; reasoning effort is adjustable through the system prompt across low, medium, high and xhigh modes. Two 4-bit quantised builds ship alongside the BF16 weights: K-Quant-17GB targets 24GB cards such as the RTX 3090 and 4090 at roughly 1% average accuracy degradation, and K-Quant-Dynamic targets 32GB systems such as the RTX 5090 and M4/M5 Max MacBook Pro at 0.2%. A bundled DFlash speculative-decoding drafter predicts 16 tokens per forward pass for a 3.1x throughput gain on an RTX 5090 (74.9 to 233.4 tokens per second). Weights are distributed on Hugging Face with day-one support across Ollama, LM Studio, llama.cpp, vLLM, SGLang, ExecuTorch and Apple MLX, plus hosted inference from Together AI, Fireworks AI and OpenRouter. Reported results include MCP Atlas 75.5, SWE-Bench Verified 76.0 and AIME 2026 94.7, positioned against Google Gemma 4 31B and Alibaba Qwen3.6-27B.
Platform and ML engineering leaders who need agent workloads to run on owned or edge hardware — because of data residency, unpredictable per-token cloud spend, or offline requirements — and who can staff the operational work an unsupported open-weight model implies.
Production-grade agentic reasoning and tool use running entirely on a single 24GB consumer GPU under a licence with no commercial restrictions, removing both per-token API cost and third-party data exposure.
At a Glance
- Category
- AI Models & APIs
- Pricing
- Free, Usage-based
- Target Market
- CTOs, Heads of AI, Platform Engineers, ML Engineers, Enterprise Developers
- Deployment
- Open-source, Self-hosted, Edge-first, API-based
- Founded
- 2004
- Headquarters
- Menlo Park, United States
- Team Size
- 500+
Key Features
- ✓Apache 2.0 open weights
Permissive licence with no commercial usage restrictions, covering BF16 weights, quantised variants, the DFlash drafter and the perception encoder — a material change from Llama's community licence for enterprise legal review.
- ✓Single-GPU agent execution
K-Quant-17GB fits 24GB consumer cards at roughly 1% accuracy loss and K-Quant-Dynamic targets 32GB systems at 0.2%, so a full agent runs on hardware teams already own.
- ✓Integrated multimodal perception
A ~1.8B ViT-G/14 vision encoder accepting 4,096 visual tokens per image lets agents read screens, documents and UI state rather than text alone.
- ✓DFlash speculative decoding
A bundled drafter predicts 16 tokens per forward pass, delivering 3.1x throughput on an RTX 5090 and 1.8x on an M5 Max, which is what makes local always-on agents usable.
- ✓Adjustable reasoning modes
Low, medium, high and xhigh reasoning effort is selectable from the system prompt, letting one deployment trade latency against accuracy per task type.
- ✓131K+ token context with agent-trained recovery
Long context plus training on failure recovery means the model retries alternative execution paths when an API call or terminal command errors instead of terminating the trajectory.
- ✓Broad day-one runtime support
Ollama, LM Studio, llama.cpp, vLLM, SGLang, ExecuTorch, Apple MLX and PyTorch TorchTitan for fine-tuning, plus hosted endpoints on Together AI, Fireworks AI and OpenRouter.
Capabilities
Use Cases
- •Local coding agent
Developers run a SWE-Bench-capable coding agent on a workstation GPU, keeping proprietary source code off third-party inference endpoints entirely.
- •Offline and air-gapped operations agents
Defence, industrial and healthcare teams deploy agents that operate on-site with no outbound network dependency, satisfying strict data-residency rules.
- •High-volume LLM-as-a-judge evaluation
Evaluation and grading pipelines that would cost heavily on per-token APIs run locally at fixed hardware cost across millions of samples.
- •Screen-reading and document agents
The bundled vision encoder lets agents interpret dashboards, scanned forms and application UIs without a separate OCR or vision service in the pipeline.
- •Fine-tuned vertical agents
Teams post-train on domain data with TorchTitan and redistribute the derivative commercially, which the Apache 2.0 licence explicitly permits.
- •Cost-tiered inference routing
Enterprises route routine agent steps to a self-hosted Muse Glimmer and escalate only hard reasoning steps to a frontier cloud model.
Ideal For
Best For
- ✓Running always-on local or edge agents that need tool calls and failure recovery without a cloud round trip
- ✓Regulated or air-gapped environments where prompts and documents cannot leave owned infrastructure
- ✓Replacing high-volume, low-complexity cloud API calls where per-token billing dominates the AI budget
- ✓On-device coding assistants and terminal agents on developer workstations with 24-32GB of VRAM
- ✓LLM-as-a-judge evaluation pipelines run at scale where hosted inference would be prohibitively expensive
- ✓Teams that need to fine-tune and redistribute a derivative model under a permissive licence
Not Ideal For
- ✗Teams needing frontier-class ceiling performance — Meta classifies it as 'moderate or lower risk', not a frontier-tier model, and it trails Qwen3.6-27B on OSWorld-Verified computer-use and TerminalBench 2.1 terminal tasks
- ✗Organisations without GPU operations capability: there is no vendor SLA, no support contract and no managed uptime, so you own inference reliability end to end
- ✗Laptops and workstations with 8-16GB of memory, which remain unsupported even at 4-bit quantisation
- ✗Security-sensitive agent deployments without added guardrails — Meta's own card reports a 28.4% prompt-injection attack success rate on Siren AgentDojo and a 26.4 CI Memories privacy violation rate, both worse than Gemma 4
Integrations
Deployment
Market Analysis
Pros
- ✓Apache 2.0 is the most permissive licence Meta has released for a model of this size, eliminating a common procurement blocker
- ✓Runs a full agent loop on a single 24GB consumer GPU, converting variable per-token API spend into fixed hardware cost
- ✓Day-one support across the entire local-inference ecosystem plus three hosted providers, so evaluation does not require custom serving work
- ✓Strong reported agentic and reasoning scores — MCP Atlas 75.5, SWE-Bench Verified 76.0, AIME 2026 94.7
- ✓Bundled DFlash drafter delivers a measured 3.1x speedup on an RTX 5090, which is the difference between a demo and an always-on agent
Cons
- ✗Safety posture is weaker than its closest peer: Meta's own reporting shows a 26.4 CI Memories privacy violation rate against Gemma 4's 12.1, and a 28.4% prompt-injection attack success rate on Siren AgentDojo against Gemma's 25.6%, so guardrails and human confirmation for irreversible actions are mandatory rather than optional
- ✗Does not sweep its benchmark set — VentureBeat's comparison has it trailing Qwen3.6-27B on OSWorld-Verified computer use (65.9 vs 75.6) and TerminalBench 2.1 (51.7 vs 60.7), which are exactly the tasks a desktop agent performs
- ✗No vendor support, SLA or indemnity of any kind; open weights mean the operational burden and the liability both sit with the adopter
- ✗Hardware floor excludes most corporate laptops — 8-16GB machines are unsupported, so 'runs locally' means on a workstation or a well-specified Mac, not on standard issue fleet hardware
- ✗Knowledge cutoff of 4 January 2026 means retrieval or tool access is required for anything current
Pricing
Open weights (Apache 2.0)
$0
- ✓BF16 full-precision weights
- ✓K-Quant-17GB and K-Quant-Dynamic 4-bit builds
- ✓DFlash speculative-decoding drafter
- ✓Perception (vision) encoder
- ✓Commercial use and redistribution permitted
- ✓No vendor support or SLA
Hosted inference via third-party providers
Usage-based (varies by provider)
- ✓Together AI, Fireworks AI and OpenRouter endpoints
- ✓Per-token metering set by each provider
- ✓No infrastructure to operate
- ✓Provider-dependent SLAs
The weights themselves are free under Apache 2.0 with no seat, token or revenue restrictions, so the real cost is hardware and operations: a 24GB consumer GPU is the practical floor for the K-Quant-17GB build and 32GB for K-Quant-Dynamic, while full BF16 precision needs 55GB or more. Meta publishes no list price and sells no support contract, so enterprises either absorb inference operations internally or pay per-token rates set independently by Together AI, Fireworks AI or OpenRouter. There is no enterprise tier to buy.
Security & Compliance
Connect
Sources
This page was written from 6 sources, 5 on domains other than developer.meta.com.
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