Model ID
meta-models/Muse-Glimmer-30B
Capabilities
Muse Glimmer 30B
Muse Glimmer 30B is a multimodal dense causal language model designed for autonomous agentic tasks, combining multi-step reasoning, reliable tool use, failure recovery, and visual understanding. It has 30B parameters, including a dedicated 1.8B-parameter perception encoder, with a 52-layer language model using a 6,656 hidden size and 32 attention heads. The language model uses a repeating 3:1 local-to-global attention pattern with a 2,048-token sliding window and GQA with 32 query and 2 KV heads. It supports text and image inputs with a 131K+ context window and is optimized for local deployment and long-horizon agent workflows.
About this model
Muse Glimmer 30B is a multimodal dense causal language model designed for autonomous agentic tasks, combining multi-step reasoning, reliable tool use, failure recovery, and visual understanding. It has 30B parameters, including a dedicated 1.8B-parameter perception encoder, with a 52-layer language model using a 6,656 hidden size and 32 attention heads. The language model uses a repeating 3:1 local-to-global attention pattern with a 2,048-token sliding window and GQA with 32 query and 2 KV heads. It supports text and image inputs with a 131K+ context window and is optimized for local deployment and long-horizon agent workflows.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (meta-models/Muse-Glimmer-30B). Released under Apache 2.0. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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