Meta 发布了 Muse Glimmer，一个基于 Apache 2.0 许可的新型 30B 参数开源模型。该模型专为端到端代理任务完成、可靠的工具使用和多步推理而设计，在 DeepSearch QA 和 SWE-Bench 等基准测试中表现强劲。然而，其内存需求（压缩后约 24-32 GB）可能会限制其在许多消费级笔记本电脑上的本地使用，而早期基准测试表明其效率可能低于 Qwen 等竞争对手。 AI
影响 为代理任务完成领域的开源模型树立了新标杆，但高内存需求可能会限制其普及。
排序理由 发布了带有系统卡的 Frontier-lab 模型。
AI 生成摘要 · Google Gemini · 来自 14 个来源。 我们如何撰写摘要 →
<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 …
<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/1vmskes/qwen36_35b_2_min_vs_muse_glimmer_30b_4_min_on/"> <img alt="Qwen3.6 35B (2 min) vs Muse Glimmer 30B (4 min) on custom Llama.cpp build (RTX 5080)" src="https://external-preview.redd.it/c2xxdmQ0bHRvMGpoMT…
<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…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vmaofu/intel_llmscaler_ready_with_muse_glimmer_support/"> <img alt="Intel LLM-Scaler ready with Muse Glimmer support, other LLMs & features" src="https://external-preview.redd.it/2-UNwyNZomjn63oZR-3ih1IDi…
<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-…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vkxpnd/muse_glimmer_benchmark/"> <img alt="Muse glimmer benchmark" src="https://preview.redd.it/h7tyoi3p6mih1.png?width=140&height=140&crop=1:1,smart&auto=webp&s=bfdb27ed7cc7552a299c8e1e33546a…
<!-- 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…