Meta AIMuse Glimmer 30B Assistant
Muse Glimmer 30B Assistant is a 30-billion-parameter chat model built by Meta AI, designed for autonomous agentic tasks on consumer hardware. It integrates multimodal understanding, accepting both text and image inputs, and employs a DFlash block-diffusion technique for faster generation.
Input
Output
Context
131K
Max Output
-
Parameters
2.6B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Muse Glimmer 30B Assistant
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Muse Glimmer 30B Assistant
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Muse Glimmer 30B Assistant advertises a context window of 131,072 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Muse Glimmer 30B Assistant is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Muse Glimmer 30B Assistant accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Muse Glimmer 30B Assistant is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Muse Glimmer 30B Assistant is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 3.0 GB at INT8 for the weights and a default context, from the catalog's 2.6B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 4, 2026
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