Local personal agents
Run multi-step workflows on capable workstations where privacy, offline access, or control matters.
Independent tool overview
Muse Glimmer at a glance
Muse Glimmer is Meta's 30-billion-parameter open-weight model for local computer agents, coding, tool use, multimodal reasoning, and model evaluation. Apache 2.0 weights are free to download, while practical local use generally calls for a 24 GB or 32 GB memory envelope with Meta's quantized releases.
Visit the official Muse Glimmer site ↗.webp&w=3840&q=75)
- Parameters
- 29.6B including vision encoder
- Context
- 131,072+ tokens
- Input / output
- Text and images / text
- Local target
- 24 GB or 32 GB quantized envelope
- License
- Apache 2.0
- Knowledge cutoff
- January 4, 2026
Overview
What Muse Glimmer is
Muse Glimmer combines a dense language model with a dedicated perception encoder so an agent can reason over text and images, call structured tools, recover from failed actions, and sustain multi-step workflows. It supports a 131,072-plus-token context window, more than 100 training languages, adjustable reasoning effort, and common agent scaffolds.
Its main appeal is deployment control. Meta provides full-precision and 4-bit weights, including a roughly 17 GB quantized variant and a DFlash speculative-decoding drafter. That can keep inference and sensitive context on the user's device, but the model is not a finished personal assistant: developers must supply the tool layer, permissions, memory, confirmations, sandboxing, monitoring, and application-specific safety tests.
Use cases
Who Muse Glimmer is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Tool-calling systems
Build agents that need structured function calls, retry behavior, long context, and controllable reasoning effort.
Local coding agents
Use an open model for repository work, debugging, terminal tasks, and controlled software-engineering experiments.
Multimodal document workflows
Interpret screenshots, charts, forms, and documents alongside instructions and tool results.
Model evaluation and synthetic data
Use Glimmer as an LLM judge or a source of training examples after validating bias and scoring consistency for the domain.
Capabilities
Core Muse Glimmer features
Open weights
Full and quantized artifacts are released under Apache 2.0 for commercial and research use.
Agentic task training
Post-training targets end-to-end tasks, multi-step planning, tool use, coding, and recovery after failed calls.
Dedicated perception encoder
A roughly 1.8B-parameter vision encoder accepts interleaved screenshots, charts, documents, images, and text.
Long context
Supports at least 131,072 tokens for larger workspaces, tool histories, documents, and multi-turn agent state.
Controllable reasoning
System prompts can select low, medium, high, or xhigh reasoning strength to trade latency for deeper work.
Failure recovery
The model is trained to diagnose unexpected tool output and retry instead of ending the workflow immediately.
Quantized local releases
Meta provides approximately 4-bit variants aimed at 24 GB and 32 GB hardware envelopes.
DFlash speculative decoding
A companion drafter proposes blocks of tokens that the main model verifies to improve generation speed on supported runtimes.
Broad runtime support
Released artifacts can run through Transformers, vLLM, SGLang, and compatible local-app or quantization ecosystems.
Hosted inference
Providers including Together AI and Fireworks offer serverless access for teams that do not want to manage local GPUs.
Process
How the Muse Glimmer workflow works
Step 1
Choose the deployment
Decide between local quantized inference, a dedicated server, or a hosted API based on privacy, throughput, latency, and operations.
Step 2
Select an artifact
Use full precision for maximum fidelity and sufficient GPU memory, or choose a 4-bit release matched to the available memory envelope.
Step 3
Build the scaffold
Connect the model to carefully described tools, state, logs, timeouts, retry policies, and a limited execution environment.
Step 4
Set explicit permissions
Default to read-only access, scope credentials narrowly, and require user confirmation before irreversible or external actions.
Step 5
Evaluate the real workflow
Create held-out tasks for accuracy, prompt injection, privacy, recovery, latency, cost, and failure severity in the intended domain.
Step 6
Monitor and update
Track tool failures, unsafe attempts, drift, dependency changes, and user overrides, then rerun regression tests after every system change.
Cost
Muse Glimmer pricing and free plan
Muse Glimmer's model weights carry no license fee under Apache 2.0, but local hardware and operations are not free. As of August 30, 2026, Together AI and Fireworks both list serverless usage at $0.35 per million input tokens, $0.04 per million cached input tokens, and $1.50 per million output tokens.
Open weights
$0 model license
Download full-precision or quantized weights under Apache 2.0 and run them on your own infrastructure.
- Commercial and research use
- Hardware, power, storage, and engineering costs remain
- Review the license and usage policy
Together AI serverless
$0.35 input / $1.50 output per 1M tokens
Hosted API access without managing the inference server.
- Cached input: $0.04 per 1M tokens
- Usage-based billing
- Provider terms and limits apply
Fireworks serverless
$0.35 input / $1.50 output per 1M tokens
Serverless multimodal inference with a 131K context listing.
- Cached input: $0.04 per 1M tokens
- Dedicated deployments available separately
- Provider terms and limits apply
Pricing checked . Check current pricing at the source ↗
Assessment
Muse Glimmer strengths and limitations
Where it stands out
- Open Apache 2.0 weights permit broad commercial and research experimentation.
- A 30B dense model is more locally approachable than frontier-scale agent models.
- Text-plus-image input suits agents that must inspect screens, charts, and documents.
- Long context can hold substantial task state and tool history.
- Training explicitly targets tool use, multi-step execution, coding, and recovery.
- Quantized releases and a speculative drafter reduce the local deployment barrier.
- Multiple runtimes and hosted providers give teams flexible deployment paths.
- Meta publishes architecture, benchmark, safety, artifact, and hardware details in the model card.
What to consider
- Muse Glimmer is a model, not a complete agent application; the developer must build and secure the surrounding system.
- A practical quantized deployment still needs roughly a 24 GB or 32 GB memory envelope, beyond many consumer computers.
- Full-precision operation is substantially heavier and Meta targets roughly 64 GB of VRAM.
- Local inference does not guarantee privacy if the scaffold sends prompts, files, telemetry, or tool calls to external services.
- The model can produce inaccurate, biased, objectionable, or overconfident output.
- Multi-step reasoning and tool recovery can fail in novel situations not represented in training.
- Agent permissions create risks of data loss, unwanted messages, purchases, account changes, and credential exposure.
- Meta's own security results show that prompt-injection and information-flow violations remain material concerns.
- Published benchmark numbers are author-reported and can depend heavily on scaffolds, prompts, tools, graders, and evaluation settings.
- Quantization can introduce small quality differences, particularly in edge cases.
- Video is not explicitly optimized and is processed as individual frames; audio input and output are unsupported.
- Performance across more than 100 training languages is uneven because not every language was fully evaluated.
- Meta says the model is not intended to be downloaded or used directly by people under 18.
- The January 2026 knowledge cutoff means current facts still require retrieval from trusted sources.
Compare
Muse Glimmer alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
Qwen3.6-27B
Use Qwen3.6-27B for another similarly sized open model with especially strong coding performance and a longer 256K context.
Explore Qwen3.6-27B →Consumer
Gemma 4
Use Gemma 4 when you want Google's open-weight family across multiple size points and reasoning configurations.
Explore Gemma 4 →Consumer
Muse Spark 1.1
Use Muse Spark 1.1 when cloud deployment, a much larger context window, and higher agent capability matter more than local weights.
Explore Muse Spark 1.1 →Consumer
Mistral 3
Use Mistral 3 for an Apache-licensed family spanning small edge models through a much larger general model.
Explore Mistral 3 →Questions
Muse Glimmer FAQs
What is Muse Glimmer?
Muse Glimmer is Meta's 29.6B-parameter open-weight multimodal model for local agents, tool calling, coding, long-horizon tasks, synthetic data, and LLM-as-a-judge workflows.
Can Muse Glimmer run locally?
Yes, on sufficiently capable hardware. Meta's quantized releases target 24 GB and 32 GB memory envelopes; many ordinary laptops and consumer GPUs do not meet that requirement.
How much memory does the 17 GB version need?
The weight file is roughly 17 GB, but inference also needs memory for the KV cache, vision encoder, runtime, and optional drafter. Meta positions that artifact for a 24 GB envelope rather than a 17 GB machine.
Is Muse Glimmer free?
The model weights have no license fee under Apache 2.0. You still pay for local hardware and operations or for tokens if you use a hosted inference provider.
Does Muse Glimmer support images?
Yes. It accepts interleaved text and image input through a dedicated perception encoder and produces text output. It does not support audio, and video is handled as individual frames.
What is Muse Glimmer's context window?
The official model card lists a context length of 131,072 tokens or more.
Is a local Muse Glimmer agent automatically safe?
No. Local inference can reduce data exposure, but a tool-using agent still needs sandboxing, least-privilege credentials, prompt-injection defenses, confirmation for irreversible actions, and application-specific evaluation.
How does Muse Glimmer compare with Qwen3.6-27B and Gemma 4 31B?
Meta reports competitive or stronger results on several agentic benchmarks and mixed results on others. Buyers should rerun representative tasks because benchmark outcomes depend on the scaffold and do not establish a universal winner.
Bottom line
Our Muse Glimmer verdict
Muse Glimmer is a compelling open-weight base for teams that want a capable multimodal agent model under their own control and can supply 24 GB-class hardware or hosted inference. Its real value will come from the scaffold around it: narrow tools, strong permissions, held-out evaluations, prompt-injection defenses, and human confirmation for consequential actions.
Visit Muse Glimmer website ↗Similar Consumer Tools
View all Consumer AI tools →
Hint
Hint - Martha Stewart's AI home app for maintenance, repairs, and fair quotes
Nemotron 3.5 Lightning
Nemotron 3.5 Lightning - Nvidia's small open model for agents' high-volume grunt work
Inkling
Inkling - Thinking Machines' open-weight multimodal model with adjustable reasoning
.jpeg)
Grok 4.6
Grok 4.6 - SpaceXAI's new near-frontier model with strong agentic capabilities
AI training for the future of work.
Get access to all our AI courses, hundreds of real-world AI use cases, live expert-led workshops, an exclusive network of AI early adopters, and more.
AI Courses
Get unlimited access to all of our current & upcoming industry-specific AI courses for the duration of your subscription.
Daily Guides
To keep up with the rapid pace of AI, our team publishes AI implementation guides daily. Our library contains 300+ practical use cases to automate real-world work.
Workshops
Join weekly, live, interactive sessions with industry leaders who are at the forefront of AI for hands-on implementation guidance and exclusive insights.
Community
Network with an exclusive community of AI-first professionals who are working smarter with AI. Learn how early adopters are using AI in their work and businesses.