{"content_id":"vupqjenc6z","slug":"meta-muse-glimmer-local-ai-agent-model","locale":"en","schema_type":"TechArticle","category":"ai_data","category_name":"AI Data","title":"Meta Muse Glimmer: Key Facts About a 30-Billion-Parameter Local AI Model for Laptops","summary":"Muse Glimmer was introduced as a multimodal model quantized to run approximately 30 billion parameters on consumer hardware and designed as a long-running, tool-using local AI agent. However, because the memory, speed, and benchmark figures are Meta's own measurements cited in the provided materials, they need to be verified through the official model card and independent evaluations.","author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["Muse Glimmer aims to be a long-running AI agent that handles files, screens, and tools, rather than merely a local chatbot.","According to the provided materials, the 4-bit quantized version was designed to run the entire system in an environment with about 24GB or 32GB of memory.","Speculative Decoding using a DFlash drafter processes multiple candidate tokens at once, subject to verification by the main model.","Local processing can reduce external data transmission, but it does not eliminate the risks of prompt injection, excessive system permissions, or file corruption.","Performance and licensing should be assessed only after reviewing the official repository's model card, the actual license file, and independent hardware-specific measurements."],"content_markdown":"Meta Muse Glimmer was introduced as a model designed to run a model with approximately 30 billion parameters on a personal PC or high-performance laptop and build long-running local AI agents on top of it. Its core goal is to go beyond text generation by understanding images and screens and using tools such as files, functions, and terminals across multiple steps.\n\nThe product specifications and benchmark figures in this article are based on Meta announcements cited in the provided materials. Because direct URLs to the original article and official model card were not provided, the figures should not be interpreted as independently verified results.\n\n## Key Specifications of Muse Glimmer\n\nThe main specifications described in the provided materials are as follows.\n\n| Item | Introduced details | Points to consider when interpreting |\n|---|---|---|\n| Model size | Approximately 30 billion parameters | The actual number of active parameters and detailed architecture need to be checked in the model card |\n| Input formats | Text and images | Supported image resolution, frame count, and vision token costs need to be checked separately |\n| Context | At least up to 131,072 tokens | Accuracy and memory usage at the maximum length may differ from those with short inputs |\n| Languages | At least 100 languages | This does not mean quality is identical across languages |\n| Low-precision versions | K-Quant-17GB, K-Quant-Dynamic | The capacity included in the name must be distinguished from total runtime memory |\n| Primary uses | Coding, desktop automation, function calling, document analysis, evaluation | Actual capabilities depend on the connected runtime and permission policies |\n| Acceleration method | Speculative Decoding using the DFlash drafter | The degree of acceleration varies depending on hardware and input length |\n| Release formats | BF16, 4-bit quantization, drafter model | Available files and supported runtimes need to be checked in the repository |\n| License | Introduced as Apache License 2.0 | The actual LICENSE and any additional terms of use in the model repository need to be checked |\n\n## How 30 Billion Parameters Can Run in 24GB\n\n### A Simple Calculation of Weight Memory\n\nConsidering only the model weights, the required storage can be estimated as follows.\n\n- BF16 or FP16: 30 billion × 2 bytes = approximately 60GB\n- 8-bit: 30 billion × 1 byte = approximately 30GB\n- 4-bit: 30 billion × 0.5 bytes = approximately 15GB\n\nQuantization scales, metadata, alignment, and runtime overhead are added to 4-bit weights. Therefore, the approximately 17GB weight package mentioned in the provided materials may be larger than the theoretical 15GB.\n\n### Memory Required Beyond the Weights\n\nHaving a 17GB model file does not mean it can run directly on a device with 17GB of memory. Actual inference additionally uses space for the following components.\n\n- The KV cache that preserves input and output history\n- A vision encoder that processes image inputs\n- Intermediate activations and runtime workspace\n- Auxiliary models such as the DFlash drafter\n- Memory used by the operating system, desktop, and other applications\n- Buffers and copy space between GPU and system memory\n\nThe provided materials describe K-Quant-17GB as a version designed for an approximately 24GB environment and K-Quant-Dynamic as one designed for an approximately 32GB environment. However, actual execution instructions must be checked to determine whether this memory refers to dedicated VRAM, unified memory such as Apple Silicon, or a configuration that also uses part of the system RAM.\n\nThe KV cache also grows as the context becomes longer. Therefore, a specification stating support for 131,072 tokens alone does not establish that the maximum length can always be used in a 24GB environment.\n\n## Why It Was Designed as a Local AI Agent\n\nThe type of agent Muse Glimmer aims to support differs from a chatbot that answers a question once and stops. A typical workflow is as follows.\n\n1. Interpret the user's goals and constraints.\n2. Plan the subtasks required for completion.\n3. Call file search, function, terminal, or browser tools.\n4. Read execution results and error messages.\n5. Revise the plan or code.\n6. Run testing or verification tools again.\n7. Repeat the process until the completion criteria are met.\n\nFor example, when fixing a project error, source analysis, command execution, log reading, code changes, testing, and further revisions occur in sequence. Because model inference is repeated at each step, not only response speed but also the accuracy of tool calls, failure recovery capabilities, and state retention are important.\n\n## Training Methods and Agent Capabilities\n\nAccording to the provided materials, Muse Glimmer was pretrained using a distillation method that leveraged the outputs of a larger teacher model, Muse Spark. Data for long contexts and agent tasks was subsequently added, and supervised learning, on-policy distillation, and reinforcement learning were reportedly used for post-training.\n\nThe general role of each method can be understood as follows.\n\n- **Distillation:** Trains a smaller model to imitate the outputs or behavior of a larger teacher model.\n- **Supervised learning:** Provides desirable responses, function calls, or task procedures as correct examples.\n- **On-policy learning:** Corrects errors based on behavioral trajectories actually generated by the current model.\n- **Reinforcement learning:** Optimizes rewards such as task success, correct tool use, and compliance with safety rules.\n\nThese training procedures do not automatically guarantee an agent's success rate. The scope of the training data, evaluation environment, tool definitions, and execution sandbox have a major impact on the results.\n\n## Multimodal Capabilities and Applications\n\nMuse Glimmer was introduced as a multimodal model that understands image inputs in addition to text. Expected inputs and use cases include the following.\n\n| Visual input | Example possible tasks |\n|---|---|\n| PC screen capture | Interpret error messages or UI status |\n| Document image | Extract and summarize the contents of tables, paragraphs, and forms |\n| Graphs and charts | Read and explain axes, legends, and trends |\n| GUI screen | Identify buttons and input fields to plan the next action |\n| Development tool screen | Analyze terminal output or debugger status |\n| Multiple files and images | Compare documents and retain records of long-running tasks |\n\nVisual understanding and actual computer operation are separate capabilities. Even if the model interprets a screen, operating a mouse or keyboard requires a separate agent runtime, accessibility interface, or automation tool.\n\n## DFlash and Speculative Decoding\n\nAutoregressive language models generally calculate the next token sequentially based on previously generated tokens. Speculative Decoding works by having a smaller drafter model propose multiple candidate tokens first and having the main model verify those candidates at once.\n\nThe process can be summarized as follows.\n\n1. The DFlash drafter proposes a group of tokens likely to come next.\n2. The main Muse Glimmer model verifies the proposed tokens.\n3. Tokens that match the main model's distribution are accepted.\n4. Generation resumes from the point where they do not match.\n\nWhen an exact verification procedure is used, generation time can be reduced while preserving the main model's output distribution. However, if the drafter's prediction hit rate is low or memory bandwidth is insufficient, the speedup may fall short of expectations.\n\n## Generation Speeds Presented in the Provided Materials\n\nThe following figures were introduced as Meta's own measurements using the DFlash drafter with K-Quant-17GB. They are not independent benchmarks, and direct comparisons are limited when hardware settings, prompts, context length, runtime, and power conditions are not provided.\n\n| Hardware | Baseline speed | With DFlash | Reported improvement |\n|---|---:|---:|---:|\n| NVIDIA GeForce RTX 5090 | 74.9 tokens/second | 233.4 tokens/second | Approximately 3.1× |\n| Apple M5 Max | 26.6 tokens/second | 50.2 tokens/second | Approximately 1.8× |\n| Apple M4 Max | 23.7 tokens/second | 37.8 tokens/second | Approximately 1.5× |\n\nBecause an agent performs inference multiple times and waits for external tools, token generation speed does not equal the total task time. Actual completion time also includes prompt processing speed, file input/output, code execution, network access, and the number of tool retries.\n\n## How to Read the Benchmark Results\n\nThe provided materials compare Muse Glimmer with Gemma 4 31B and Qwen 3.6 27B and present the following results.\n\n| Benchmark | Muse Glimmer | Gemma 4 31B | Qwen 3.6 27B |\n|---|---:|---:|---:|\n| MCP Atlas | 75.5 | 54.2 | 62.5 |\n| DeepSearch QA | 74.6 | No figure in the materials | No figure in the materials |\n\nAt the same time, Qwen 3.6 27B was reported to have achieved higher results than Muse Glimmer on OSWorld Verified, TerminalBench 2.1, and SWE-bench Verified. This means that task-specific evaluations are more important than a single average score.\n\nThe following should be checked when reviewing benchmarks.\n\n- Were the same model precision and quantization conditions used?\n- Were the number of tool calls and time limits identical?\n- Were the agent prompts and orchestration code disclosed?\n- Were the image resolution and maximum context the same?\n- Were averages and variance across multiple runs provided?\n- Was the possibility that evaluation data was included in the training data examined?\n- Were failed tasks left without human correction or restarting?\n\nAccording to the provided materials, across an average of 15 benchmarks, K-Quant-Dynamic showed an approximately 0.2% performance decrease relative to the original, while K-Quant-17GB showed an approximately 1% decrease. These figures were also introduced as values from Meta's own evaluations, and the decline for individual tasks may differ from the average.\n\n## Privacy Benefits and Limitations of Local Execution\n\nA fully local configuration can help create a system that does not transmit source code, internal documents, screen captures, or database contents to an external LLM API. It also offers the advantages of operating on an isolated network and avoiding external API usage fees charged per token.\n\nHowever, the fact that the model file is local does not mean all data flows remain local. The following components may connect to external services.\n\n- Web search or remote browser tools\n- Error and usage analytics telemetry\n- Extensions and agent plugins\n- Cloud-based document repositories\n- Package managers and code repositories\n- Remote embedding, search, or evaluation services\n\nOrganizations handling sensitive information should inspect network logs and running processes and review the data paths of all connected tools, not just the model runtime.\n\n## Security Risks and Mitigations for Local Agents\n\nLocal AI can reduce transmission risks, but risks arising from system permissions may instead increase. Particular attention should be paid to indirect prompt injection, in which instructions hidden in external documents or web pages alter the agent's original goal.\n\nRecommended defensive measures include the following.\n\n- Run it in a read-only workspace first.\n- Allow access only to required folders rather than the entire home directory.\n- Require human approval for deletion, overwriting, fund transfers, and deployment.\n- Block administrator privileges and access to core operating system folders.\n- Do not place secret keys or authentication tokens directly in the model context.\n- Apply allowlists and execution time limits to terminal commands.\n- Treat text from external documents as untrusted data.\n- Create a snapshot or version-control commit before making changes.\n- Record all tool calls and file changes in audit logs.\n- Test it in a container or virtual machine separated from the actual production environment.\n\nIn healthcare, finance, law, defense, and the public sector, local processing alone does not complete regulatory compliance. Access controls, record retention, approval by responsible personnel, data classification, and model validation are also required.\n\n## What an Apache 2.0 Release Means\n\nThe provided materials state that the Muse Glimmer weights were released under Apache License 2.0. Apache 2.0 is generally a permissive license that allows use, modification, distribution, and commercial use and includes explicit patent provisions. Redistribution must comply with conditions such as retaining a copy of the license, preserving copyright notices, and indicating changes.\n\nHowever, the following need to be checked separately when actually using the model.\n\n- Whether each model file is truly covered by Apache 2.0\n- Whether the model card or repository includes additional usage restrictions\n- Whether the included code and tokenizer use the same license\n- Whether the vision encoder and drafter model have separate terms\n- Whether trademark rights or third-party data rights are included within the scope of the permission\n\nReleasing the weights does not mean that the entire training process is open source. According to the provided materials, the complete training data and complete training code were not released. Therefore, while Muse Glimmer can be called an open-weight model, it should not be characterized as a fully open-source model whose entire training process can be reproduced.\n\n## Checklist Before Adoption\n\nWhen evaluating the product, results reproducing your own tasks are more important than the maximum speed stated in press materials.\n\n1. Verify the official distributor and model repository.\n2. Check the architecture, context, supported languages, and input formats in the model card.\n3. Have legal personnel review the LICENSE file and any additional terms of use.\n4. Measure peak memory usage, including the model, KV cache, vision encoder, and drafter.\n5. Evaluate accuracy, tool success rate, and failure recovery rate using actual documents and code.\n6. Measure input processing speed and memory growth with long contexts.\n7. Block network access and verify that all features actually operate locally.\n8. Conduct attack tests using prompt injection and malicious files.\n9. Apply human approval and automatic backups to important changes.\n10. Compare task-specific quality differences between the quantized versions and the original BF16 version.\n\n## Overall Assessment\n\nMuse Glimmer's distinguishing feature lies less in a claim that it is a general-purpose model with the highest scores across all benchmarks and more in its design goal of bringing a multimodal model with approximately 30 billion parameters and long-running agent capabilities to consumer hardware. If 4-bit quantization, long context, DFlash acceleration, and a permissive license are provided as stated in the official materials, it could become a meaningful option for local coding, document analysis, and automation on isolated networks.\n\nOn the other hand, references to 24GB or 32GB do not guarantee that all model capabilities and the maximum context will operate smoothly on every device. Until the official model card and reproducible benchmarks are available, the speed and quality figures should be distinguished as claims based on Meta's own measurements, and memory, security, and accuracy should be evaluated using actual workloads.","content_html":"\u003cp\u003eMeta Muse Glimmer was introduced as a model designed to run a model with approximately 30 billion parameters on a personal PC or high-performance laptop and build long-running local AI agents on top of it. Its core goal is to go beyond text generation by understanding images and screens and using tools such as files, functions, and terminals across multiple steps.\u003c/p\u003e\n\u003cp\u003eThe product specifications and benchmark figures in this article are based on Meta announcements cited in the provided materials. Because direct URLs to the original article and official model card were not provided, the figures should not be interpreted as independently verified results.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#key-specifications-of-muse-glimmer\" class=\"anchor\" id=\"key-specifications-of-muse-glimmer\"\u003e\u003c/a\u003eKey Specifications of Muse Glimmer\u003c/h2\u003e\n\u003cp\u003eThe main specifications described in the provided materials are as follows.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eItem\u003c/th\u003e\n\u003cth\u003eIntroduced details\u003c/th\u003e\n\u003cth\u003ePoints to consider when interpreting\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eModel size\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eApproximately 30 billion parameters\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eThe actual number of active parameters and detailed architecture need to be checked in the model card\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eInput formats\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eText and images\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eSupported image resolution, frame count, and vision token costs need to be checked separately\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eContext\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eAt least up to 131,072 tokens\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eAccuracy and memory usage at the maximum length may differ from those with short inputs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eLanguages\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eAt least 100 languages\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eThis does not mean quality is identical across languages\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eLow-precision versions\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eK-Quant-17GB, K-Quant-Dynamic\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eThe capacity included in the name must be distinguished from total runtime memory\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003ePrimary uses\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eCoding, desktop automation, function calling, document analysis, evaluation\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eActual capabilities depend on the connected runtime and permission policies\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eAcceleration method\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eSpeculative Decoding using the DFlash drafter\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eThe degree of acceleration varies depending on hardware and input length\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eRelease formats\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eBF16, 4-bit quantization, drafter model\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eAvailable files and supported runtimes need to be checked in the repository\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item\"\u003eLicense\u003c/td\u003e\n\u003ctd data-label=\"Introduced details\"\u003eIntroduced as Apache License 2.0\u003c/td\u003e\n\u003ctd data-label=\"Points to consider when interpreting\"\u003eThe actual LICENSE and any additional terms of use in the model repository need to be checked\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-30-billion-parameters-can-run-in-24gb\" class=\"anchor\" id=\"how-30-billion-parameters-can-run-in-24gb\"\u003e\u003c/a\u003eHow 30 Billion Parameters Can Run in 24GB\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#a-simple-calculation-of-weight-memory\" class=\"anchor\" id=\"a-simple-calculation-of-weight-memory\"\u003e\u003c/a\u003eA Simple Calculation of Weight Memory\u003c/h3\u003e\n\u003cp\u003eConsidering only the model weights, the required storage can be estimated as follows.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBF16 or FP16: 30 billion × 2 bytes = approximately 60GB\u003c/li\u003e\n\u003cli\u003e8-bit: 30 billion × 1 byte = approximately 30GB\u003c/li\u003e\n\u003cli\u003e4-bit: 30 billion × 0.5 bytes = approximately 15GB\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eQuantization scales, metadata, alignment, and runtime overhead are added to 4-bit weights. Therefore, the approximately 17GB weight package mentioned in the provided materials may be larger than the theoretical 15GB.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#memory-required-beyond-the-weights\" class=\"anchor\" id=\"memory-required-beyond-the-weights\"\u003e\u003c/a\u003eMemory Required Beyond the Weights\u003c/h3\u003e\n\u003cp\u003eHaving a 17GB model file does not mean it can run directly on a device with 17GB of memory. Actual inference additionally uses space for the following components.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe KV cache that preserves input and output history\u003c/li\u003e\n\u003cli\u003eA vision encoder that processes image inputs\u003c/li\u003e\n\u003cli\u003eIntermediate activations and runtime workspace\u003c/li\u003e\n\u003cli\u003eAuxiliary models such as the DFlash drafter\u003c/li\u003e\n\u003cli\u003eMemory used by the operating system, desktop, and other applications\u003c/li\u003e\n\u003cli\u003eBuffers and copy space between GPU and system memory\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe provided materials describe K-Quant-17GB as a version designed for an approximately 24GB environment and K-Quant-Dynamic as one designed for an approximately 32GB environment. However, actual execution instructions must be checked to determine whether this memory refers to dedicated VRAM, unified memory such as Apple Silicon, or a configuration that also uses part of the system RAM.\u003c/p\u003e\n\u003cp\u003eThe KV cache also grows as the context becomes longer. Therefore, a specification stating support for 131,072 tokens alone does not establish that the maximum length can always be used in a 24GB environment.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#why-it-was-designed-as-a-local-ai-agent\" class=\"anchor\" id=\"why-it-was-designed-as-a-local-ai-agent\"\u003e\u003c/a\u003eWhy It Was Designed as a Local AI Agent\u003c/h2\u003e\n\u003cp\u003eThe type of agent Muse Glimmer aims to support differs from a chatbot that answers a question once and stops. A typical workflow is as follows.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eInterpret the user's goals and constraints.\u003c/li\u003e\n\u003cli\u003ePlan the subtasks required for completion.\u003c/li\u003e\n\u003cli\u003eCall file search, function, terminal, or browser tools.\u003c/li\u003e\n\u003cli\u003eRead execution results and error messages.\u003c/li\u003e\n\u003cli\u003eRevise the plan or code.\u003c/li\u003e\n\u003cli\u003eRun testing or verification tools again.\u003c/li\u003e\n\u003cli\u003eRepeat the process until the completion criteria are met.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor example, when fixing a project error, source analysis, command execution, log reading, code changes, testing, and further revisions occur in sequence. Because model inference is repeated at each step, not only response speed but also the accuracy of tool calls, failure recovery capabilities, and state retention are important.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#training-methods-and-agent-capabilities\" class=\"anchor\" id=\"training-methods-and-agent-capabilities\"\u003e\u003c/a\u003eTraining Methods and Agent Capabilities\u003c/h2\u003e\n\u003cp\u003eAccording to the provided materials, Muse Glimmer was pretrained using a distillation method that leveraged the outputs of a larger teacher model, Muse Spark. Data for long contexts and agent tasks was subsequently added, and supervised learning, on-policy distillation, and reinforcement learning were reportedly used for post-training.\u003c/p\u003e\n\u003cp\u003eThe general role of each method can be understood as follows.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eDistillation:\u003c/strong\u003e Trains a smaller model to imitate the outputs or behavior of a larger teacher model.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupervised learning:\u003c/strong\u003e Provides desirable responses, function calls, or task procedures as correct examples.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eOn-policy learning:\u003c/strong\u003e Corrects errors based on behavioral trajectories actually generated by the current model.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eReinforcement learning:\u003c/strong\u003e Optimizes rewards such as task success, correct tool use, and compliance with safety rules.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese training procedures do not automatically guarantee an agent's success rate. The scope of the training data, evaluation environment, tool definitions, and execution sandbox have a major impact on the results.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#multimodal-capabilities-and-applications\" class=\"anchor\" id=\"multimodal-capabilities-and-applications\"\u003e\u003c/a\u003eMultimodal Capabilities and Applications\u003c/h2\u003e\n\u003cp\u003eMuse Glimmer was introduced as a multimodal model that understands image inputs in addition to text. Expected inputs and use cases include the following.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eVisual input\u003c/th\u003e\n\u003cth\u003eExample possible tasks\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Visual input\"\u003ePC screen capture\u003c/td\u003e\n\u003ctd data-label=\"Example possible tasks\"\u003eInterpret error messages or UI status\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Visual input\"\u003eDocument image\u003c/td\u003e\n\u003ctd data-label=\"Example possible tasks\"\u003eExtract and summarize the contents of tables, paragraphs, and forms\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Visual input\"\u003eGraphs and charts\u003c/td\u003e\n\u003ctd data-label=\"Example possible tasks\"\u003eRead and explain axes, legends, and trends\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Visual input\"\u003eGUI screen\u003c/td\u003e\n\u003ctd data-label=\"Example possible tasks\"\u003eIdentify buttons and input fields to plan the next action\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Visual input\"\u003eDevelopment tool screen\u003c/td\u003e\n\u003ctd data-label=\"Example possible tasks\"\u003eAnalyze terminal output or debugger status\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Visual input\"\u003eMultiple files and images\u003c/td\u003e\n\u003ctd data-label=\"Example possible tasks\"\u003eCompare documents and retain records of long-running tasks\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eVisual understanding and actual computer operation are separate capabilities. Even if the model interprets a screen, operating a mouse or keyboard requires a separate agent runtime, accessibility interface, or automation tool.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#dflash-and-speculative-decoding\" class=\"anchor\" id=\"dflash-and-speculative-decoding\"\u003e\u003c/a\u003eDFlash and Speculative Decoding\u003c/h2\u003e\n\u003cp\u003eAutoregressive language models generally calculate the next token sequentially based on previously generated tokens. Speculative Decoding works by having a smaller drafter model propose multiple candidate tokens first and having the main model verify those candidates at once.\u003c/p\u003e\n\u003cp\u003eThe process can be summarized as follows.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe DFlash drafter proposes a group of tokens likely to come next.\u003c/li\u003e\n\u003cli\u003eThe main Muse Glimmer model verifies the proposed tokens.\u003c/li\u003e\n\u003cli\u003eTokens that match the main model's distribution are accepted.\u003c/li\u003e\n\u003cli\u003eGeneration resumes from the point where they do not match.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWhen an exact verification procedure is used, generation time can be reduced while preserving the main model's output distribution. However, if the drafter's prediction hit rate is low or memory bandwidth is insufficient, the speedup may fall short of expectations.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#generation-speeds-presented-in-the-provided-materials\" class=\"anchor\" id=\"generation-speeds-presented-in-the-provided-materials\"\u003e\u003c/a\u003eGeneration Speeds Presented in the Provided Materials\u003c/h2\u003e\n\u003cp\u003eThe following figures were introduced as Meta's own measurements using the DFlash drafter with K-Quant-17GB. They are not independent benchmarks, and direct comparisons are limited when hardware settings, prompts, context length, runtime, and power conditions are not provided.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eHardware\u003c/th\u003e\n\u003cth\u003eBaseline speed\u003c/th\u003e\n\u003cth\u003eWith DFlash\u003c/th\u003e\n\u003cth\u003eReported improvement\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Hardware\"\u003eNVIDIA GeForce RTX 5090\u003c/td\u003e\n\u003ctd data-label=\"Baseline speed\"\u003e74.9 tokens/second\u003c/td\u003e\n\u003ctd data-label=\"With DFlash\"\u003e233.4 tokens/second\u003c/td\u003e\n\u003ctd data-label=\"Reported improvement\"\u003eApproximately 3.1×\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Hardware\"\u003eApple M5 Max\u003c/td\u003e\n\u003ctd data-label=\"Baseline speed\"\u003e26.6 tokens/second\u003c/td\u003e\n\u003ctd data-label=\"With DFlash\"\u003e50.2 tokens/second\u003c/td\u003e\n\u003ctd data-label=\"Reported improvement\"\u003eApproximately 1.8×\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Hardware\"\u003eApple M4 Max\u003c/td\u003e\n\u003ctd data-label=\"Baseline speed\"\u003e23.7 tokens/second\u003c/td\u003e\n\u003ctd data-label=\"With DFlash\"\u003e37.8 tokens/second\u003c/td\u003e\n\u003ctd data-label=\"Reported improvement\"\u003eApproximately 1.5×\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eBecause an agent performs inference multiple times and waits for external tools, token generation speed does not equal the total task time. Actual completion time also includes prompt processing speed, file input/output, code execution, network access, and the number of tool retries.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#how-to-read-the-benchmark-results\" class=\"anchor\" id=\"how-to-read-the-benchmark-results\"\u003e\u003c/a\u003eHow to Read the Benchmark Results\u003c/h2\u003e\n\u003cp\u003eThe provided materials compare Muse Glimmer with Gemma 4 31B and Qwen 3.6 27B and present the following results.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eBenchmark\u003c/th\u003e\n\u003cth\u003eMuse Glimmer\u003c/th\u003e\n\u003cth\u003eGemma 4 31B\u003c/th\u003e\n\u003cth\u003eQwen 3.6 27B\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Benchmark\"\u003eMCP Atlas\u003c/td\u003e\n\u003ctd data-label=\"Muse Glimmer\"\u003e75.5\u003c/td\u003e\n\u003ctd data-label=\"Gemma 4 31B\"\u003e54.2\u003c/td\u003e\n\u003ctd data-label=\"Qwen 3.6 27B\"\u003e62.5\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Benchmark\"\u003eDeepSearch QA\u003c/td\u003e\n\u003ctd data-label=\"Muse Glimmer\"\u003e74.6\u003c/td\u003e\n\u003ctd data-label=\"Gemma 4 31B\"\u003eNo figure in the materials\u003c/td\u003e\n\u003ctd data-label=\"Qwen 3.6 27B\"\u003eNo figure in the materials\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eAt the same time, Qwen 3.6 27B was reported to have achieved higher results than Muse Glimmer on OSWorld Verified, TerminalBench 2.1, and SWE-bench Verified. This means that task-specific evaluations are more important than a single average score.\u003c/p\u003e\n\u003cp\u003eThe following should be checked when reviewing benchmarks.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWere the same model precision and quantization conditions used?\u003c/li\u003e\n\u003cli\u003eWere the number of tool calls and time limits identical?\u003c/li\u003e\n\u003cli\u003eWere the agent prompts and orchestration code disclosed?\u003c/li\u003e\n\u003cli\u003eWere the image resolution and maximum context the same?\u003c/li\u003e\n\u003cli\u003eWere averages and variance across multiple runs provided?\u003c/li\u003e\n\u003cli\u003eWas the possibility that evaluation data was included in the training data examined?\u003c/li\u003e\n\u003cli\u003eWere failed tasks left without human correction or restarting?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAccording to the provided materials, across an average of 15 benchmarks, K-Quant-Dynamic showed an approximately 0.2% performance decrease relative to the original, while K-Quant-17GB showed an approximately 1% decrease. These figures were also introduced as values from Meta's own evaluations, and the decline for individual tasks may differ from the average.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#privacy-benefits-and-limitations-of-local-execution\" class=\"anchor\" id=\"privacy-benefits-and-limitations-of-local-execution\"\u003e\u003c/a\u003ePrivacy Benefits and Limitations of Local Execution\u003c/h2\u003e\n\u003cp\u003eA fully local configuration can help create a system that does not transmit source code, internal documents, screen captures, or database contents to an external LLM API. It also offers the advantages of operating on an isolated network and avoiding external API usage fees charged per token.\u003c/p\u003e\n\u003cp\u003eHowever, the fact that the model file is local does not mean all data flows remain local. The following components may connect to external services.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWeb search or remote browser tools\u003c/li\u003e\n\u003cli\u003eError and usage analytics telemetry\u003c/li\u003e\n\u003cli\u003eExtensions and agent plugins\u003c/li\u003e\n\u003cli\u003eCloud-based document repositories\u003c/li\u003e\n\u003cli\u003ePackage managers and code repositories\u003c/li\u003e\n\u003cli\u003eRemote embedding, search, or evaluation services\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOrganizations handling sensitive information should inspect network logs and running processes and review the data paths of all connected tools, not just the model runtime.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#security-risks-and-mitigations-for-local-agents\" class=\"anchor\" id=\"security-risks-and-mitigations-for-local-agents\"\u003e\u003c/a\u003eSecurity Risks and Mitigations for Local Agents\u003c/h2\u003e\n\u003cp\u003eLocal AI can reduce transmission risks, but risks arising from system permissions may instead increase. Particular attention should be paid to indirect prompt injection, in which instructions hidden in external documents or web pages alter the agent's original goal.\u003c/p\u003e\n\u003cp\u003eRecommended defensive measures include the following.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRun it in a read-only workspace first.\u003c/li\u003e\n\u003cli\u003eAllow access only to required folders rather than the entire home directory.\u003c/li\u003e\n\u003cli\u003eRequire human approval for deletion, overwriting, fund transfers, and deployment.\u003c/li\u003e\n\u003cli\u003eBlock administrator privileges and access to core operating system folders.\u003c/li\u003e\n\u003cli\u003eDo not place secret keys or authentication tokens directly in the model context.\u003c/li\u003e\n\u003cli\u003eApply allowlists and execution time limits to terminal commands.\u003c/li\u003e\n\u003cli\u003eTreat text from external documents as untrusted data.\u003c/li\u003e\n\u003cli\u003eCreate a snapshot or version-control commit before making changes.\u003c/li\u003e\n\u003cli\u003eRecord all tool calls and file changes in audit logs.\u003c/li\u003e\n\u003cli\u003eTest it in a container or virtual machine separated from the actual production environment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn healthcare, finance, law, defense, and the public sector, local processing alone does not complete regulatory compliance. Access controls, record retention, approval by responsible personnel, data classification, and model validation are also required.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#what-an-apache-20-release-means\" class=\"anchor\" id=\"what-an-apache-20-release-means\"\u003e\u003c/a\u003eWhat an Apache 2.0 Release Means\u003c/h2\u003e\n\u003cp\u003eThe provided materials state that the Muse Glimmer weights were released under Apache License 2.0. Apache 2.0 is generally a permissive license that allows use, modification, distribution, and commercial use and includes explicit patent provisions. Redistribution must comply with conditions such as retaining a copy of the license, preserving copyright notices, and indicating changes.\u003c/p\u003e\n\u003cp\u003eHowever, the following need to be checked separately when actually using the model.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhether each model file is truly covered by Apache 2.0\u003c/li\u003e\n\u003cli\u003eWhether the model card or repository includes additional usage restrictions\u003c/li\u003e\n\u003cli\u003eWhether the included code and tokenizer use the same license\u003c/li\u003e\n\u003cli\u003eWhether the vision encoder and drafter model have separate terms\u003c/li\u003e\n\u003cli\u003eWhether trademark rights or third-party data rights are included within the scope of the permission\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eReleasing the weights does not mean that the entire training process is open source. According to the provided materials, the complete training data and complete training code were not released. Therefore, while Muse Glimmer can be called an open-weight model, it should not be characterized as a fully open-source model whose entire training process can be reproduced.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#checklist-before-adoption\" class=\"anchor\" id=\"checklist-before-adoption\"\u003e\u003c/a\u003eChecklist Before Adoption\u003c/h2\u003e\n\u003cp\u003eWhen evaluating the product, results reproducing your own tasks are more important than the maximum speed stated in press materials.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eVerify the official distributor and model repository.\u003c/li\u003e\n\u003cli\u003eCheck the architecture, context, supported languages, and input formats in the model card.\u003c/li\u003e\n\u003cli\u003eHave legal personnel review the LICENSE file and any additional terms of use.\u003c/li\u003e\n\u003cli\u003eMeasure peak memory usage, including the model, KV cache, vision encoder, and drafter.\u003c/li\u003e\n\u003cli\u003eEvaluate accuracy, tool success rate, and failure recovery rate using actual documents and code.\u003c/li\u003e\n\u003cli\u003eMeasure input processing speed and memory growth with long contexts.\u003c/li\u003e\n\u003cli\u003eBlock network access and verify that all features actually operate locally.\u003c/li\u003e\n\u003cli\u003eConduct attack tests using prompt injection and malicious files.\u003c/li\u003e\n\u003cli\u003eApply human approval and automatic backups to important changes.\u003c/li\u003e\n\u003cli\u003eCompare task-specific quality differences between the quantized versions and the original BF16 version.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003e\n\u003ca href=\"#overall-assessment\" class=\"anchor\" id=\"overall-assessment\"\u003e\u003c/a\u003eOverall Assessment\u003c/h2\u003e\n\u003cp\u003eMuse Glimmer's distinguishing feature lies less in a claim that it is a general-purpose model with the highest scores across all benchmarks and more in its design goal of bringing a multimodal model with approximately 30 billion parameters and long-running agent capabilities to consumer hardware. If 4-bit quantization, long context, DFlash acceleration, and a permissive license are provided as stated in the official materials, it could become a meaningful option for local coding, document analysis, and automation on isolated networks.\u003c/p\u003e\n\u003cp\u003eOn the other hand, references to 24GB or 32GB do not guarantee that all model capabilities and the maximum context will operate smoothly on every device. Until the official model card and reproducible benchmarks are available, the speed and quality figures should be distinguished as claims based on Meta's own measurements, and memory, security, and accuracy should be evaluated using actual workloads.\u003c/p\u003e\n","tags":["AI Agents","Meta","Muse Glimmer","Local AI","Quantization"],"faqs":[{"question":"How is Muse Glimmer different from typical local LLMs?","answer":"Unlike chatbots that only generate text responses, its main goal is to serve as a long-running AI agent that analyzes files and screens, invokes tools such as functions or terminals, checks the results, and revises its plans."},{"question":"Can a 30-billion-parameter model really run with 24GB of memory?","answer":"The provided materials explain that the 4-bit K-Quant-17GB version was optimized for an environment with about 24GB of memory. However, the required memory varies depending on the context length, vision input, KV cache, drafter model, and operating system usage, so 24GB should not be interpreted as a guaranteed minimum requirement under all usage conditions."},{"question":"Why are the 17GB model size and 24GB runtime memory requirement different?","answer":"17GB primarily refers to the quantized weights package. Actual execution additionally requires memory for the KV cache, intermediate activations, vision encoder, runtime workspace, and operating system."},{"question":"Can the 131,072-token context always be used?","answer":"Even if the model supports that length, the actual maximum may be limited by the runtime, memory, KV cache precision, and amount of image input. Whether information retrieval accuracy is maintained at the maximum length must also be evaluated separately."},{"question":"Does the DFlash drafter reduce the model's response quality?","answer":"Speculative Decoding is designed so that the main model verifies the tokens proposed by the drafter, so when implemented correctly, it can preserve the main model's output distribution. However, the results and degree of acceleration may vary depending on the implementation method and sampling settings."},{"question":"If it runs locally, does that mean data never leaves the device?","answer":"No. Even if the model is local, web searches, plugins, remote repositories, telemetry, or cloud embedding tools may transmit data. The network communications of the entire agent setup must be reviewed."},{"question":"What permissions are safe to grant a local AI agent?","answer":"It is advisable to grant minimum permissions only to the necessary work folders and initially run it in read-only mode. Human approval should be required for high-risk tasks such as deleting files, transmitting data externally, installing software, and deploying."},{"question":"Does Apache 2.0 allow unrestricted commercial use?","answer":"Apache 2.0 itself is a permissive license that allows commercial use, modification, and redistribution. However, you must check the LICENSE and model card in the official repository to determine whether that license applies to the actual model files and whether there are additional terms or third-party components."},{"question":"Is Muse Glimmer a fully open-source model?","answer":"According to the provided materials, the weights have been released, but the complete training data and training code have not. Therefore, it can be described as an open-weight model, but a distinction must be made as to whether it is a fully open-source model whose entire training process can be reproduced."},{"question":"Can Meta's reported speeds and benchmarks be taken at face value?","answer":"Internal measurements are useful as initial reference material but are not a substitute for independent verification. Reproduction results using the same quantization, runtime, context length, power settings, and agent tools are needed."}],"sources":[{"url":"https://www.apache.org/licenses/LICENSE-2.0","title":"Apache License, Version 2.0","type":"source"},{"url":"https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf","title":"NIST AI 600-1: Artificial Intelligence Risk Management Framework—Generative Artificial Intelligence Profile","type":"source"}],"images":[{"id":602,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NzE2MywicHVyIjoiYmxvYl9pZCJ9fQ==--655d2556aee6b63b88d70cffbc019429039bcd2b/ai-7afa8941.webp","is_representative":true,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"데이터 블록을 받아 문서·이미지·코드로 처리하는 로컬 AI 노트북 개념도","caption":"노트북에서 로컬 AI가 다양한 데이터를 처리하고 보안 워크플로를 수행하는 모습을 나타낸다.","description":null},"en":{"alt":"Local AI laptop processing data blocks into documents, images, folders, code, and charts","caption":"The diagram depicts a laptop running local AI for multimodal processing and secure workflows.","description":null},"ja":{"alt":"データブロックを文書・画像・コードなどに処理するローカルAI搭載ノートPCの概念図","caption":"ノートPC上のローカルAIが多様なデータを処理し、安全なワークフローを実行する様子を示す。","description":null},"es":{"alt":"Portátil con IA local que procesa bloques de datos en documentos, imágenes, código y gráficos","caption":"El diagrama muestra una IA local en un portátil gestionando datos multimodales y flujos seguros.","description":null},"id":{"alt":"Laptop AI lokal memproses blok data menjadi dokumen, gambar, folder, kode, dan grafik","caption":"Diagram ini menggambarkan AI lokal di laptop yang menangani data multimodal dan alur kerja aman.","description":null},"pt":{"alt":"Notebook com IA local processando blocos de dados em documentos, imagens, códigos e gráficos","caption":"O diagrama mostra uma IA local no notebook gerenciando dados multimodais e fluxos seguros.","description":null},"zh-hant":{"alt":"本機 AI 筆電將資料區塊處理成文件、圖像、資料夾、程式碼與圖表","caption":"示意圖呈現筆電上的本機 AI 處理多模態資料並執行安全工作流程。","description":null},"de":{"alt":"Laptop mit lokaler KI verarbeitet Datenblöcke zu Dokumenten, Bildern, Code und Diagrammen","caption":"Die Grafik zeigt lokale KI auf einem Laptop bei der multimodalen Verarbeitung und sicheren Abläufen.","description":null}}},{"id":603,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NzE2OSwicHVyIjoiYmxvYl9pZCJ9fQ==--febd3177afd7cf52d6fa7bb0e86da563e46d0ffb/ai-34107f0a.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"AI 칩이 표시된 노트북과 보안 방패, 파일, 경고, 성능 계기판을 배치한 일러스트","caption":"노트북에서 실행되는 로컬 AI의 보안, 파일 처리, 최적화 및 성능 측정을 시각화했다.","description":null},"en":{"alt":"Laptop with an AI chip, security shield, files, warnings, and performance gauges","caption":"The illustration visualizes security, file processing, optimization, and performance for local AI on a laptop.","description":null},"ja":{"alt":"AIチップを表示したノートPCと、セキュリティ盾、ファイル、警告、性能メーターの図","caption":"ノートPCで動作するローカルAIの安全性、ファイル処理、最適化、性能測定を表している。","description":null},"es":{"alt":"Portátil con chip de IA, escudo de seguridad, archivos, alertas y medidores de rendimiento","caption":"La ilustración representa la seguridad, el procesamiento, la optimización y el rendimiento de una IA local.","description":null},"id":{"alt":"Laptop dengan cip AI, perisai keamanan, berkas, peringatan, dan pengukur kinerja","caption":"Ilustrasi ini menggambarkan keamanan, pemrosesan berkas, optimasi, dan kinerja AI lokal di laptop.","description":null},"pt":{"alt":"Notebook com chip de IA, escudo de segurança, arquivos, alertas e medidores de desempenho","caption":"A ilustração mostra segurança, processamento de arquivos, otimização e desempenho de IA local no notebook.","description":null},"zh-hant":{"alt":"顯示 AI 晶片的筆電，周圍有安全盾牌、檔案、警告與效能儀表","caption":"插圖呈現筆電本機 AI 的安全性、檔案處理、最佳化與效能測量。","description":null},"de":{"alt":"Laptop mit KI-Chip, Sicherheitsschild, Dateien, Warnsymbolen und Leistungsanzeigen","caption":"Die Illustration zeigt Sicherheit, Dateiverarbeitung, Optimierung und Leistung lokaler KI auf einem Laptop.","description":null}}}],"published_at":"2026-08-12T14:00:06+09:00","updated_at":"2026-08-12T14:00:06+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant","de"],"url":"https://injoys.com/en/articles/meta-muse-glimmer-local-ai-agent-model"}