Meta's Muse Glimmer 30B Runs a Real Agent on a Single 3090
Quick verdict
Meta released Muse Glimmer, a 30B dense multimodal model under Apache 2.0, and it is the first Meta open-weight drop in a while that people actually want to run. The pitch is local agents: a model small enough to sit on one consumer GPU, fast enough to feel responsive, and tuned from the start for long tool-use loops rather than chat. It is not the smartest model on the board, but for self-hosted agent work on hardware you already own, it changes what is realistic.
What actually shipped
The launch came from Mark Zuckerberg and Alexandr Wang, with Meta framing it as a return to broadly available open weights. The concrete details:
- 30B dense, multimodal (interleaved text and image), Apache 2.0, with controllable reasoning effort and 100+ language support.
- Built for local deployment. Around 60GB at BF16, roughly 18GB at 4-bit, and a 128K context window that stays cheap thanks to memory-efficient hybrid attention.
- A bundled DFlash speculative-decoding drafter for faster on-device generation, plus quantization that brings the language model under 20GB.
- Trained on agentic traces from the outset and logit-distilled from Muse Spark, so it is not the usual base-then-post-train release. Meta also promised Muse Spark 1.2 weights soon.
The number that matters for expectations: Artificial Analysis puts Glimmer at 35 on its Intelligence Index, just behind Qwen3.6-27B at 38 and near Kimi K2.5 at 36, with a strong 44 on its Openness Index. Their read is that it punches above its size for local self-hosting and does well on Tau3-Banking tool use, but trails peers on hallucination and knowledge calibration. Treat it as an agent workhorse, not an oracle.
Why it matters
The headline for most people is the hardware bar. One tester reported Muse Glimmer running on a single RTX 3090 in about 22 to 23 GiB of VRAM at 128K context, sustaining roughly 64 to 124 tokens per second of generation. On a MacBook M5 Max, another cited around 50 tokens per second with quantization and speculative decoding. That is not a demo running in a datacenter. That is a capable agent model on a $700 used GPU or a laptop.
For anyone weighing a monthly API bill against local compute, that shifts the math. A 30B model you can run offline, with no per-token cost and full data control, is a real option for the high-volume, lower-stakes end of an agent workload: bulk categorization, first-pass drafting, tool routing. You keep a frontier model for the parts that need it and hand the grind to Glimmer. That routing pattern is exactly where a lot of teams are already trying to cut spend.
Where it lands vs the field
| Metric | Muse Glimmer 30B |
|---|---|
| Artificial Analysis Intelligence Index | 35 (near Kimi K2.5, behind Qwen3.6-27B at 38) |
| Openness Index | 44 |
| Memory footprint | ~60GB BF16, ~18GB 4-bit |
| Context window | 128K, hybrid-attention KV cache |
| Local throughput | ~64-124 tok/s on RTX 3090, ~50 tok/s on M5 Max |
| License | Apache 2.0 |
The honest read: Glimmer is not going to beat a hosted frontier model on hard reasoning, and community testers already flag Qwen3.6 as stronger on raw intelligence. Its edge is the combination of size, speed, license, and agent tuning landing at once, with day-zero support across vLLM, llama.cpp, Ollama, Together AI, Hugging Face transformers, and Unsloth. That breadth on launch day is unusual and it is why the model got traction fast.
Video: Muse Glimmer running locally
For a hands-on look at Glimmer on a single GPU, watch this.
FAQ
What is Muse Glimmer?
Meta's 30B dense, multimodal, agent-focused open-weight model, released under Apache 2.0. It is tuned for always-on local agents and can run on a single consumer GPU with a 128K context window.
Can it really run on one RTX 3090?
Yes. Testers report the 4-bit GGUF running in about 22 to 23 GiB of VRAM at full 128K context, with generation around 64 to 124 tokens per second. The efficient hybrid-attention KV cache is what makes long context affordable on 24GB cards.
Is it as good as Qwen3.6 or a frontier model?
No. On the Artificial Analysis Intelligence Index it sits at 35, behind Qwen3.6-27B at 38 and well behind hosted frontier models. Its strength is local agent work and tool use, not top-tier reasoning or factual recall.
Sources
- @finkd - Zuckerberg on Muse Glimmer and Spark 1.2
- @alexandr_wang - the Muse Glimmer launch thread
- @AIatMeta - official model details and download links
- @ArtificialAnlys - Intelligence Index, footprint, and context benchmarks
- r/LocalLLaMA - Muse Glimmer fits on a single RTX 3090
Further reading
Try all the models mentioned in this article
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