MODEL PROFILE

Muse-Glimmer-30B-NVFP4

NVIDIA · Muse-Glimmer-30B-NVFP4

The NVIDIA Muse-Glimmer-30B NVFP4 model is the quantized version of Meta Superintelligence Lab's Muse-Glimmer-30B model, which is an auto-regressive language model that uses an optimized transformer architecture. Muse Glimmer is a dense causal transformer with a dedicated perception encoder for interleaved text and image input, purpose-built for local agentic tasks including multi-step reasoning, tool use, and multimodal understanding. For more information, please check here. The NVIDIA Muse-Glimmer-30B NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (Muse-Glimmer-30B) Model Card from Meta. Developers building autonomous agentic systems, multi-step planning agents, coding agents, and multimodal reasoning applications requiring reliable tool use and failure recovery on consumer hardware.

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:21
OVERVIEW

Model overview

The NVIDIA Muse-Glimmer-30B NVFP4 model is the quantized version of Meta Superintelligence Lab's Muse-Glimmer-30B model, which is an auto-regressive language model that uses an optimized transformer architecture. Muse Glimmer is a dense causal transformer with a dedicated perception encoder for interleaved text and image input, purpose-built for local agentic tasks including multi-step reasoning, tool use, and multimodal understanding. For more information, please check here. The NVIDIA Muse-Glimmer-30B NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (Muse-Glimmer-30B) Model Card from Meta. Developers building autonomous agentic systems, multi-step planning agents, coding agents, and multimodal reasoning applications requiring reliable tool use and failure recovery on consumer hardware.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。