Meta releases open-weight Muse Glimmer model for agentic tasks
ByPulseAugur Editorial·[14 sources]·
Meta has released Muse Glimmer, a new 30B parameter open-weight model licensed under Apache 2.0. The model is designed for end-to-end agentic task completion, reliable tool use, and multi-step reasoning, showing strong performance on benchmarks like DeepSearch QA and SWE-Bench. However, its memory requirements, around 24-32 GB after compression, may limit its local usability on many consumer laptops, and early benchmarks suggest it may be less efficient than competitors like Qwen.
AI
IMPACT
Sets a new standard for open-weight models in agentic task completion, though high memory requirements may limit adoption.
RANK_REASON
Frontier-lab model release with system card.
<p><strong><a href="https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model">Introducing Muse Glimmer</a></strong></p> Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama …
Towards AI
TIER_1English(EN)·Gian Luca Bailo, Ph.D.·
<p><em>Meta’s first Superintelligence Labs model shipped this morning. By the afternoon it was generating tokens on a six-year-old Ryzen with two consumer GPUs. Here is what it actually does — and the three walls you hit getting there.</em></p><figure><img alt="Wide 2:1 editorial…
Muse Glimmer requires 24-32 GB of memory after 4-bit compression. That leaves most laptops with 8-16 GB unable to run it locally. Performance benchmarks outside Meta's own testing remain sparse. https://www. implicator.ai/meta-releases-30 b-open-weight-muse-glimmer-and-promises-s…
<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>Non-native speaker, writing my post by hand, let me know if I make mistakes (can only learn from it!)</p> <p>Muse Glimmer 30B is so far quite nice, but I haven't found a clear-cut case yet what I can use it for over Gemma 4 3…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vmo2sp/metas_muse_glimmer_30b_now_runs_up_to_33x_faster/"> <img alt="Meta's Muse Glimmer 30B now runs up to ~3.3x faster on Mac with mlx-dspark" src="https://preview.redd.it/sp15xbwhwzih1.png?width=640&cr…
<p>A reported 30-billion-parameter model that can fit in about 24 GB of VRAM in four-bit-class form sounds like a local-agent milestone. For practitioners, it is better treated as permission to test—not permission to deploy.</p> <h2> Start with the workflow, not the model card </…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vlsixl/local_benchmark_muse_glimmer_30b_vs_qwen_36_27b/"> <img alt="Local Benchmark : Muse Glimmer 30B vs Qwen 3.6 27B vs Gemma4 31B (and many other models and finetunes)" src="https://preview.redd.it/pyodj3c…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vlmnd4/muse_glimmer_30b_running_locally_inbrowser_with/"> <img alt="Muse Glimmer 30B running locally in-browser with custom WebGPU kernels at ~25 tok/s on an M4 Max (same speed as llama.cpp)" src="https://ext…
<!-- SC_OFF --><div class="md"><p>I'm guessing that many people have been waiting for this comparison. For clarity, both models are running at full FP16 KV-cache. Due to VRAM limitations, Muse Glimmer is running full 262,144 context, whilst Qwen3.6 27B can only run at 147,500 con…
<!-- SC_OFF --><div class="md"><p>Hey. Just tried it on my old ass gpus 😄 Surprisingly Tensor Split is working on 2 gpus almost doubling PP (wonder how it will work with 4 gpus)</p> <h4>Q6 — 1 GPU</h4> <p><code> llama-server \ --model <MODEL_DIR>/Muse-Glimmer-30B-GGUF/Muse-…
<!-- SC_OFF --><div class="md"><p>I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and said its done. With my experience its not even c…
