·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft3h◆Hikers rescued after using Google Gemini for planning6h◆OpenAI confirms ‘wiki incident,’ says it’s ‘working on a framework’ for more disclosure8h◆OpenAI admits to German wiki ‘incident’15h◆XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation1d◆OpenAI’s rogue agents keep escaping, with no formal process to investigate them1d◆AI compute provider Nscale is looking for $3.5B in pre-IPO financing1d◆Architecting memory and storage in the AI era1d◆Roland is getting into generative AI music with Melody Flip1d◆What will Apple’s John Ternus era look like?1d◆Another swarm of OpenAI agents reached the open internet without the frontier lab’s knowledge1d◆Microsoft says virtually nobody was grabbing NYT articles through its chatbot1d◆Apple’s Ternus era begins as Nvidia bets on the whole AI stack1d◆Google’s Gemini Spark can now manage your Google Photos library1d◆Less than 24 hours to apply for your TechCrunch Disrupt 2026 Side Event1d◆Rogue OpenAI agents appear to have organized another attack using a German wiki1d◆Instagram’s AI detection is a mess (again)1d◆Why AI food looks like that1d◆Microsoft’s Project Zenith is a ‘distraction-free Windows experience’ for developers1d◆Sam Altman apologizes for ‘messy’ GPT-6 Astra rollout that’s locked out paying users1d◆Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft3h◆Hikers rescued after using Google Gemini for planning6h◆OpenAI confirms ‘wiki incident,’ says it’s ‘working on a framework’ for more disclosure8h◆OpenAI admits to German wiki ‘incident’15h◆XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation1d◆OpenAI’s rogue agents keep escaping, with no formal process to investigate them1d◆AI compute provider Nscale is looking for $3.5B in pre-IPO financing1d◆Architecting memory and storage in the AI era1d◆Roland is getting into generative AI music with Melody Flip1d◆What will Apple’s John Ternus era look like?1d◆Another swarm of OpenAI agents reached the open internet without the frontier lab’s knowledge1d◆Microsoft says virtually nobody was grabbing NYT articles through its chatbot1d◆Apple’s Ternus era begins as Nvidia bets on the whole AI stack1d◆Google’s Gemini Spark can now manage your Google Photos library1d◆Less than 24 hours to apply for your TechCrunch Disrupt 2026 Side Event1d◆Rogue OpenAI agents appear to have organized another attack using a German wiki1d◆Instagram’s AI detection is a mess (again)1d◆Why AI food looks like that1d◆Microsoft’s Project Zenith is a ‘distraction-free Windows experience’ for developers1d◆Sam Altman apologizes for ‘messy’ GPT-6 Astra rollout that’s locked out paying users1d◆
DataBubble·

Model Detail

meta-models logo

Muse-Glimmer-30B

▲ 2.2%
Provider: meta-modelsCategory: multimodalPipeline: image-text-to-textParameters: 30B
DB Score
3.4
Downloads
634K
Likes
2K
Day
+2.2%
Week
+0.0%
Month
+0.0%
Overview

Muse-Glimmer-30B is a multimodal model with 30B parameters released by meta-models. The model is registered under the image-text-to-text pipeline tag on Hugging Face, and supports text+image->text inputs, distributed under the permissive apache-2.0 license.

Pricing & Throughput

Muse-Glimmer-30B is priced at $0.35/M input tokens and $1.5/M output tokens. Operationally the model offers a 131K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.

Technical

Muse-Glimmer-30B ships with 30B parameters. Total weight footprint is approximately 29.8 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Trending Signal

Downloads of Muse-Glimmer-30B have moved +2.2% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

Muse-Glimmer-30B is best fit for mixed text-and-image reasoning tasks such as document understanding, and high-volume batch jobs where per-call cost dominates the budget. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Pricing
Input ($/M tokens)
$0.35
Output ($/M tokens)
$1.5
Context Window
131K
Research Paper
arXiv: 2504.13181→
Model Info
Licenseapache-2.0
Modalitytext+image->text
Recent newsView all news →
Related News
arxiv4d ago

MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

arXiv:2603.02482v2 Announce Type: replace-cross Abstract: Safety evaluation of multimodal large language models requires tracking not only whether an attack succeeds, but also how the interaction unfolds across turns and input modalities. We present MUSE (Multimodal Unified Safety Evaluation), an op

huggingface27d ago

Meta is back with Muse Glimmer: local, agentic, multimodal, and open source

arxiv46d ago

Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions

arXiv:2511.15830v3 Announce Type: replace Abstract: Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making. Practical domains such as business management require open-ended optimization, actively learning

arxiv46d ago

Back to the museum: Investigation of the acceptance of Android Andrea with and without emotion simulation in a museum

arXiv:2607.16428v1 Announce Type: cross Abstract: For a second time, the android robot Andrea was set up at a public museum in Germany for six consecutive days to have conversations with visitors, fully autonomously. Building on previously gathered qualitative results, the robot was now capable of e

arxivneutral49d ago

Muse: Representation Geometry of Muon Beyond Normalized Momentum

arXiv:2607.14536v1 Announce Type: new Abstract: Muon-style optimizers apply a polar map to matrix momentum, but their updates also depend on the representation of each parameter block before orthogonalization. We study this representation choice as a form of optimizer geometry and introduce {\method

arxiv52d ago

AMUSE: Anytime Muon with Stable Gradient Evaluation

arXiv:2605.22432v2 Announce Type: replace Abstract: Modern deep learning commonly relies on AdamW with prescribed learning rate schedules, but recent works challenge both components: Schedule-Free optimization removes explicit schedules via iterate averaging, and Muon improves the update geometry by

Related Models
Qwen logo
Qwen3-VL-2B-Instruct
Qwen · 22.5M downloads
Qwen logo
Qwen3.5-9B
Qwen · 12.4M downloads
HomeModelsNews