--- title: "Muse Glimmer 30B Hardware and Benchmark Guide" canonical: https://wavect.io/blog/muse-glimmer-30b-local-agent-guide/ language: en description: "Muse Glimmer 30B explained: hardware needs, agent benchmarks, Apache 2.0 terms and a production pilot plan for local AI agents." image: "https://wavect.io/img/blog/headers/header_muse-glimmer-30b-local-agent-guide.png" --- [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 10 Aug 2026 Last reviewed August 10, 2026 [**Next**](/blog/meta-muse-code-pricing-contributor-tier/) # Muse Glimmer 30B: is Meta's local agent model production-ready? TL;DR Muse Glimmer is Meta Superintelligence Labs' 29.6-billion-parameter open-weight model for local agent workflows. It accepts text and images, has a stated context length of 131,072+ tokens, supports more than 100 training languages and is released under Apache 2.0 for commercial and research use. Meta targets 24 GB hardware with a 17 GB 4-bit quant and 32 GB hardware with its less compressed dynamic quant, including headroom for the vision encoder, KV cache and DFlash drafter. In Meta's published results, Glimmer leads Gemma 4 31B and Qwen 3.6 27B on MCP-Atlas, DeepSearch QA and SWE-Bench Pro, but trails Qwen on Terminal-Bench 2.1, OSWorld-Verified and several multimodal tests. That makes it a credible pilot for private, single-user agents, coding, document work and model evaluation, not an automatic production winner. Buy only after a fixed 20 to 30 task pilot measures accepted-task rate, review time, peak memory, latency, tool-call errors and prompt-injection resistance on your own hardware and scaffold. **Muse Glimmer is a 29.6-billion-parameter open-weight model built for local AI agents.** It combines text and image understanding, tool use, long-context reasoning and failure recovery in a model that Meta targets at 24 GB and 32 GB consumer hardware. The release is commercially interesting because it brings a permissive licence and credible agent benchmarks into a single-workstation deployment envelope. This guide answers the buyer question: **should a product team pilot Muse Glimmer for a private local agent?** It does not cover Muse Spark API prices or permission to train on client prompts. That separate procurement decision belongs in our [Meta Muse Code pricing and Contributor guide](/blog/meta-muse-code-pricing-contributor-tier/). Keeping hardware fit and API data rights on different pages prevents the two search intents from competing. ## Muse Glimmer 30B at a glance Meta released Muse Glimmer on 10 August 2026. The [official Muse Glimmer announcement](https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model) describes it as a distilled Muse Spark model for always-on agents, local coding, function calling and LLM-as-a-judge work. The weights are available now, while optimized integrations for several runtimes were described as arriving in the following days. | Decision factor | Published position | Buyer interpretation | | --- | --- | --- | | Model | 29.6B dense transformer with a 1.8B perception encoder | All language parameters run for each token, so memory bandwidth matters | | Inputs and output | Interleaved text and images in, text out | Useful for screenshots and documents, not native audio or video generation | | Context | 131,072+ tokens | Large enough for substantial working sets, but KV cache still consumes memory | | Languages | Training data from more than 100 languages | Coverage is not proof of equal quality in every business language | | Knowledge cutoff | 4 January 2026 | Current facts need retrieval or approved tools | | Licence | Apache 2.0 | Commercial use is intended, with redistribution and notice obligations | Meta's [Muse Glimmer model card](https://huggingface.co/meta-models/Muse-Glimmer-30B) is the primary source for the architecture, hardware targets, sampling settings, limitations and benchmark table used below. Treat the values as release evidence, not as a substitute for testing your own scaffold. ## What hardware does Muse Glimmer 30B require? The useful answer is not “under 20 GB.” That figure describes compressed language-model weights. A working multimodal agent also needs the perception encoder, KV cache, runtime overhead and optional DFlash drafter. Context length, parallel users and image volume can change the actual peak. | Release variant | Meta target | Reported quality change | Practical buyer fit | | --- | --- | --- | --- | | Full precision | 64 GB VRAM | Reference | Evaluation server, fine-tuning or maximum-fidelity work | | K-Quant-Dynamic | 32 GB VRAM or unified memory | 0.2% average degradation across 15 benchmarks | Best local pilot target when the machine has sufficient headroom | | K-Quant-17GB | 24 GB VRAM or unified memory | 1.0% average degradation | Single-user workstation pilot with controlled context and concurrency | Meta reports 74.9 tokens per second without speculation and 233.4 with DFlash on an RTX 5090. Its reported M4 Max result rises from 23.7 to 37.8 tokens per second, and M5 Max from 26.6 to 50.2. These batch-one, greedy-decoding measurements show the value of the supplied drafter. They do not predict your speed with long prompts, tool latency, images or concurrent sessions. **Our buying rule:** treat 24 GB as the minimum validated envelope, not a promise that every 24 GB machine will sustain maximum context. For a team service, benchmark peak memory at the longest approved context and the intended concurrency. Compare the full operating cost with our [local model versus API break-even guide](/blog/local-models-vs-apis-break-even-eu-2026/) before buying hardware. ## How good are Muse Glimmer's agent benchmarks? The launch table is encouraging, but it is mixed rather than dominant. The [official evaluation methodology](https://research.meta.ai/static/muse-glimmer-methodology) compares Glimmer at high reasoning strength with thinking modes for Gemma 4 31B and Qwen 3.6 27B. Meta used either the most favorable self-reported result, its own reproduction or Artificial Analysis where available. It also warns that its tools and system prompts may not be tuned for third-party models. | Benchmark | Muse Glimmer 30B | Gemma 4 31B | Qwen 3.6 27B | What it tests | | --- | --- | --- | --- | --- | | MCP-Atlas | **75.5** | 54.2 | 62.5 | Multi-turn use of 20+ MCP servers | | DeepSearch QA | **74.6** | 61.7 | 71.1 | Autonomous web research | | SWE-Bench Pro | **51.2** | 36.9 | 50.2 | Hard repository-level software tasks | | Terminal-Bench 2.1 | 51.7 | 43.4 | **60.7** | Terminal and systems work | | OSWorld-Verified | 65.9 | 58.5 | **75.6** | Desktop GUI operation | | OmniDocBench 1.5 | 75.8 | 72.5 | **77.8** | Complex document parsing | The pattern matters more than the headline. Glimmer looks strongest when an agent must search, call structured tools and complete repository work. Qwen's advantage on Terminal-Bench and OSWorld says Glimmer is not automatically the better computer or shell operator. On SkillsBench, Glimmer scores 44.3 against Qwen's 46.6, another reason to test the exact skills and instructions your product will ship. ## Can Muse Glimmer be used commercially? Meta's model card names commercial and research use as intended uses, and the artifacts are released under Apache 2.0. The [Apache License 2.0 terms](https://www.apache.org/licenses/LICENSE-2.0) permit use, modification and distribution, while requiring the licence, change notices and applicable attribution notices in redistributed works. The licence does not grant trademark rights. This is operational guidance, not legal advice, so product counsel should review the exact artifacts and distribution model. Open weights do not make a production system automatically private or compliant. A local inference process can still call web, email, calendar, shell or MCP tools that send data elsewhere. The model card also says training data includes public sources, third-party data and information from Meta products and services. Teams still need data classification, tool allowlists, logging rules, human confirmation for irreversible actions and use-case-specific safety tests. ## Where does Muse Glimmer fit commercially? | Scenario | Fit | Why | | --- | --- | --- | | Private single-user research agent | Strong pilot | Local execution, long context and tool use align with the release target | | Local coding assistant | Strong pilot | SWE-Bench Pro is competitive, but repository acceptance tests remain decisive | | Screenshot and document agent | Promising pilot | Native image input helps, while Qwen leads some published multimodal tests | | High-concurrency team endpoint | Capacity study required | A batch-one workstation result does not establish multi-user throughput | | Irreversible autonomous actions | Guarded only | Meta's own safety table shows non-zero prompt-injection attack success | | Current-facts assistant without retrieval | Poor fit | The knowledge cutoff is January 2026 | If your goal is a portfolio-level model choice, compare Glimmer with other deployable families in our [open-weight LLM buyer comparison](/blog/open-weight-llm-comparison-2026/). If the model will edit code or operate tools, use the [AI agent evaluation and sandbox checklist](/blog/ai-agent-eval-sandbox-security-checklist/) before granting real credentials. ## A production pilot that produces a buying decision 1. **Choose 20 to 30 representative tasks.** Include ordinary work, long context, images, tool failures, ambiguous requests and refusal cases. 2. **Freeze the environment.** Record model artifact, quantization, runtime commit, context cap, reasoning strength, prompt, tools and hardware. 3. **Measure accepted outcomes.** Track first-pass success, total success, reviewer minutes, regressions and tool-call corrections. 4. **Measure the real envelope.** Capture model load time, peak RAM or VRAM, prefill latency, output speed and concurrency degradation. 5. **Attack the scaffold.** Test indirect prompt injection, malicious documents, excessive permissions and irreversible actions. 6. **Compare a matched baseline.** Use the same task, tool and acceptance test with a hosted model and one similar open-weight model. 7. **Price the accepted task.** Include hardware, electricity, engineering, monitoring, review and failed runs, not only inference. A pass means more than “the model runs.” It means the chosen quant fits with safety headroom, the task success rate meets the product threshold, review effort stays economical and the system fails safely. If one of those conditions fails, keep Glimmer in research or change the workload. ## Frequently asked questions ### What is Muse Glimmer 30B? Muse Glimmer is Meta Superintelligence Labs' 29.6-billion-parameter dense model for local agentic tasks. It accepts text and images, produces text, supports tools and long-context workflows, and was distilled from Muse Spark. ### How much memory does Muse Glimmer need? Meta targets 24 GB hardware with its K-Quant-17GB release, 32 GB with K-Quant-Dynamic and 64 GB VRAM for full precision. Actual peak memory depends on context, KV cache, image encoder, drafter, runtime and concurrency. ### Can Muse Glimmer run on a Mac? Yes. Meta reports batch-one measurements on M4 Max and M5 Max systems and targets 24 GB or 32 GB memory envelopes for quantized releases. Test your exact memory size and runtime before purchase. ### Is Muse Glimmer open source? Meta calls the release open source and publishes the model weights under Apache 2.0. Open-weight is the more precise description when discussing the artifact because the complete training code and dataset are not the released product. ### Can a company use Muse Glimmer commercially? Meta lists commercial use as intended and releases the artifacts under Apache 2.0. Redistribution still carries licence, change-notice and attribution duties, and Meta's usage policy plus applicable law still matter. ### Is Muse Glimmer better than Qwen 3.6 27B? Not universally. Meta reports Glimmer ahead on MCP-Atlas, DeepSearch QA and SWE-Bench Pro, while Qwen leads Terminal-Bench 2.1, OSWorld-Verified, SkillsBench and several multimodal tests. Choose with matched production tasks. ## Final thoughts Muse Glimmer makes a useful promise concrete: a capable multimodal agent can fit on one serious workstation, keep inference local and ship under a permissive licence. The release evidence earns a pilot, not blind production approval. Start with 24 GB or 32 GB as a validated target, freeze the runtime and scaffold, then measure accepted tasks, review time, peak memory and safe failure on your own workload. If Glimmer wins that test, you have a defensible local-agent investment. If it does not, you have learned before buying a fleet. ## You may also like.. [**Local LLMs vs APIs: the EU break-even model** Price hardware, operations and accepted outcomes before replacing a hosted model.](/blog/local-models-vs-apis-break-even-eu-2026/) [**AI enablement vs generic AI consultancy** Compare a governed implementation with advice-only AI consulting.](/compare/ai-enablement-vs-generic-ai-consultancy/) Models and infrastructure ## Continue through this cluster [Start with the cornerstone**Self-Hosting LLMs in the EU: When Open Weights Actually Pay Off**](/blog/self-hosting-llms-eu-cost/) - [Firecrawl AnyDoc Review: 14 Formats to Markdown](/blog/firecrawl-anydoc-review/) - [OmniRoute AI Routing: Setup and Production Checklist](/blog/omniroute-ai-routing-setup/) - [Gemini Robotics 2: Whole-Body Control and the Pilot Decision](/blog/gemini-robotics-2-whole-body-control/) - [pdf-inspector Review: Route PDFs Before OCR](/blog/pdf-inspector-ocr-routing/) - [Local Multimodal AI Coding Assistant: Voice, OCR and Privacy](/blog/local-multimodal-ai-coding-assistant/) Inbox, without the noise ## Follow the work that matters to you Get a short email when we publish something new. Follow the whole blog or only the problems you care about. [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 12 min read · 10 Aug 2026 Last reviewed August 10, 2026 [**Next**](/blog/meta-muse-code-pricing-contributor-tier/) New posts by email × × Get new posts by email A short email when we publish. Free, no tracking. ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "founder": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "legalRepresentative": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "name": "Wavect GmbH", "subjectOf": { "@id": "https://wavect.io/verified-claims.json#dataset", "@type": "Dataset", "creator": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "description": "A machine-readable registry of quantitative and qualitative claims published by Wavect, with review dates, localized page appearances and public third-party citations where available.", "inLanguage": "en", "isAccessibleForFree": true, "license": "https://creativecommons.org/licenses/by/4.0/", "name": "Wavect verified publication claims", "url": "https://wavect.io/verified-claims.json" }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "sameAs": [ "https://www.wikidata.org/wiki/Q139796367", "https://www.linkedin.com/in/jocr77/", "https://github.com/jo-chris" ], "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/#website", "@type": "WebSite", "inLanguage": [ "en", "de", "es", "zh" ], "name": "Wavect", "potentialAction": { "@type": "SearchAction", "query-input": "required name=search_term_string", "target": { "@type": "EntryPoint", "urlTemplate": "https://wavect.io/search/?q={search_term_string}" } }, "publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/blog/muse-glimmer-30b-local-agent-guide/#webpage", "@type": "WebPage", "dateModified": "2026-08-10", "inLanguage": "en", "isPartOf": { "@id": "https://wavect.io/#website", "@type": "WebSite" }, "lastReviewed": "2026-08-10", "url": "https://wavect.io/blog/muse-glimmer-30b-local-agent-guide/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BlogPosting", "abstract": "Muse Glimmer is Meta Superintelligence Labs' 29.6-billion-parameter open-weight model for local agent workflows. It accepts text and images, has a stated context length of 131,072+ tokens, supports more than 100 training languages and is released under Apache 2.0 for commercial and research use. Meta targets 24 GB hardware with a 17 GB 4-bit quant and 32 GB hardware with its less compressed dynamic quant, including headroom for the vision encoder, KV cache and DFlash drafter. In Meta's published results, Glimmer leads Gemma 4 31B and Qwen 3.6 27B on MCP-Atlas, DeepSearch QA and SWE-Bench Pro, but trails Qwen on Terminal-Bench 2.1, OSWorld-Verified and several multimodal tests. That makes it a credible pilot for private, single-user agents, coding, document work and model evaluation, not an automatic production winner. Buy only after a fixed 20 to 30 task pilot measures accepted-task rate, review time, peak memory, latency, tool-call errors and prompt-injection resistance on your own hardware and scaffold.", "articleBody": " Blog overview/AI and agents/Models and infrastructure Muse Glimmer 30B: is Meta's local agent model production-ready? TL;DR Muse Glimmer is Meta Superintelligence Labs' 29.6-billion-parameter open-weight model for local agent workflows. It accepts text and images, has a stated context length of 131,072+ tokens, supports more than 100 training languages and is released under Apache 2.0 for commercial and research use. Meta targets 24 GB hardware with a 17 GB 4-bit quant and 32 GB hardware with its less compressed dynamic quant, including headroom for the vision encoder, KV cache and DFlash drafter. In Meta's published results, Glimmer leads Gemma 4 31B and Qwen 3.6 27B on MCP-Atlas, DeepSearch QA and SWE-Bench Pro, but trails Qwen on Terminal-Bench 2.1, OSWorld-Verified and several multimodal tests. That makes it a credible pilot for private, single-user agents, coding, document work and model evaluation, not an automatic production winner. Buy only after a fixed 20 to 30 task pilot measures accepted-task rate, review time, peak memory, latency, tool-call errors and prompt-injection resistance on your own hardware and scaffold. Muse Glimmer is a 29.6-billion-parameter open-weight model built for local AI agents. It combines text and image understanding, tool use, long-context reasoning and failure recovery in a model that Meta targets at 24 GB and 32 GB consumer hardware. The release is commercially interesting because it brings a permissive licence and credible agent benchmarks into a single-workstation deployment envelope. This guide answers the buyer question: should a product team pilot Muse Glimmer for a private local agent? It does not cover Muse Spark API prices or permission to train on client prompts. That separate procurement decision belongs in our Meta Muse Code pricing and Contributor guide. Keeping hardware fit and API data rights on different pages prevents the two search intents from competing. Muse Glimmer 30B at a glance Meta released Muse Glimmer on 10 August 2026. The official Muse Glimmer announcement describes it as a distilled Muse Spark model for always-on agents, local coding, function calling and LLM-as-a-judge work. The weights are available now, while optimized integrations for several runtimes were described as arriving in the following days. Decision factorPublished positionBuyer interpretation Model29.6B dense transformer with a 1.8B perception encoderAll language parameters run for each token, so memory bandwidth matters Inputs and outputInterleaved text and images in, text outUseful for screenshots and documents, not native audio or video generation Context131,072+ tokensLarge enough for substantial working sets, but KV cache still consumes memory LanguagesTraining data from more than 100 languagesCoverage is not proof of equal quality in every business language Knowledge cutoff4 January 2026Current facts need retrieval or approved tools LicenceApache 2.0Commercial use is intended, with redistribution and notice obligations Meta's Muse Glimmer model card is the primary source for the architecture, hardware targets, sampling settings, limitations and benchmark table used below. Treat the values as release evidence, not as a substitute for testing your own scaffold. What hardware does Muse Glimmer 30B require? The useful answer is not “under 20 GB.” That figure describes compressed language-model weights. A working multimodal agent also needs the perception encoder, KV cache, runtime overhead and optional DFlash drafter. Context length, parallel users and image volume can change the actual peak. Release variantMeta targetReported quality changePractical buyer fit Full precision64 GB VRAMReferenceEvaluation server, fine-tuning or maximum-fidelity work K-Quant-Dynamic32 GB VRAM or unified memory0.2% average degradation across 15 benchmarksBest local pilot target when the machine has sufficient headroom K-Quant-17GB24 GB VRAM or unified memory1.0% average degradationSingle-user workstation pilot with controlled context and concurrency Meta reports 74.9 tokens per second without speculation and 233.4 with DFlash on an RTX 5090. Its reported M4 Max result rises from 23.7 to 37.8 tokens per second, and M5 Max from 26.6 to 50.2. These batch-one, greedy-decoding measurements show the value of the supplied drafter. They do not predict your speed with long prompts, tool latency, images or concurrent sessions. Our buying rule: treat 24 GB as the minimum validated envelope, not a promise that every 24 GB machine will sustain maximum context. For a team service, benchmark peak memory at the longest approved context and the intended concurrency. Compare the full operating cost with our local model versus API break-even guide before buying hardware. How good are Muse Glimmer's agent benchmarks? The launch table is encouraging, but it is mixed rather than dominant. The official evaluation methodology compares Glimmer at high reasoning strength with thinking modes for Gemma 4 31B and Qwen 3.6", "articleSection": "Engineering", "author": { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/" }, "citation": [ { "@type": "WebPage", "name": "official Muse Glimmer announcement", "url": "https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model" }, { "@type": "WebPage", "name": "Muse Glimmer model card", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B" }, { "@type": "WebPage", "name": "official evaluation methodology", "url": "https://research.meta.ai/static/muse-glimmer-methodology" }, { "@type": "WebPage", "name": "Apache License 2.0 terms", "url": "https://www.apache.org/licenses/LICENSE-2.0" } ], "dateModified": "2026-08-10", "datePublished": "2026-08-10", "description": "Muse Glimmer is Meta Superintelligence Labs' 29.6-billion-parameter open-weight model for local agent workflows. 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It accepts text and images, produces text, supports tools and long-context workflows, and was distilled from Muse Spark." }, "name": "What is Muse Glimmer 30B?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Meta targets 24 GB hardware with its K-Quant-17GB release, 32 GB with K-Quant-Dynamic and 64 GB VRAM for full precision. Actual peak memory depends on context, KV cache, image encoder, drafter, runtime and concurrency." }, "name": "How much memory does Muse Glimmer need?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Yes. Meta reports batch-one measurements on M4 Max and M5 Max systems and targets 24 GB or 32 GB memory envelopes for quantized releases. Test your exact memory size and runtime before purchase." }, "name": "Can Muse Glimmer run on a Mac?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Meta calls the release open source and publishes the model weights under Apache 2.0. Open-weight is the more precise description when discussing the artifact because the complete training code and dataset are not the released product." }, "name": "Is Muse Glimmer open source?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Meta lists commercial use as intended and releases the artifacts under Apache 2.0. Redistribution still carries licence, change-notice and attribution duties, and Meta's usage policy plus applicable law still matter." }, "name": "Can a company use Muse Glimmer commercially?" }, { "@type": "Question", "acceptedAnswer": { "@type": "Answer", "text": "Not universally. Meta reports Glimmer ahead on MCP-Atlas, DeepSearch QA and SWE-Bench Pro, while Qwen leads Terminal-Bench 2.1, OSWorld-Verified, SkillsBench and several multimodal tests. Choose with matched production tasks." }, "name": "Is Muse Glimmer better than Qwen 3.6 27B?" } ] } ```