Catalog / Use case
196 reports of running Muse Glimmer 30B locally: GGUF and MLX quants, DFlash speculative decoding, GPU benchmarks, Mac setups and fine-tunes.
Raj
@hrhraj
Took your custom-connector idea to the physical world: the "service with an API" was my robot. Muse wrote the connector for my rover's API, then installed PyTorch & Depth Anything V2 in the VM because the camera is 2D, drove to the black ball and stopped a few inches short.
X post · Local & open models★ Pick· ♥ 1
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Unsloth AI
@UnslothAI
2-bit Muse Glimmer GGUF managed to call 100+ tools on just 14GB RAM. 🔥 Muse Glimmer did a complete repo bug hunt for 5 mins nonstop with: evidence, repro, fix, tests and a PR writeup. Run and train it in Unsloth. GitHub repo: github.com/unslothai/unsl…
X post · Local & open models★ Pick· ♥ 1.6K
atomic.chat
@atomic_chat_hq
Run Meta's new Muse Glimmer 30B♾locally with 16GB VRAM! We ship our own GGUF quants. AD-IQ3_XXS does 62 tokens/s on a single RTX 4080 with vision and DFlash, and picks the same next token as the BF16 original 90% of the time! Run the model via Atomic Chat
X post · Local & open models· ♥ 59
ROCmFP4 GGUF of Muse Glimmer 30B with DFlash for AMD Strix Halo, requiring a ROCmFPX llama.cpp fork that adds the muse-glimmer architecture.
Resource · Local & open models· ♥ 4
Kingy AI explains Muse Glimmer's benchmarks, 24-64 GB hardware needs, GGUF setup, pricing, runtimes and risks.
Resource · Local & open models
Meta's lightweight DFlash block-diffusion drafter for Muse Glimmer 30B that predicts blocks of 16 tokens per forward pass for speculative decoding.
Resource · Local & open models· ♥ 61
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Together AI
@togethercompute
Muse Glimmer is now live on Together AI. We’re proud to be a Day 0 launch partner for this open-weight model from Meta Superintelligence Labs, built for long-running agents that can reason, use tools, recover, and keep working across complex tasks.
X post · Local & open models· ♥ 35
AI at Meta
@AIatMeta
Muse Spark 1.2 supports a broad range of multimodal tasks, from turning visuals into working code to translating perception into physical action. It also brings robust audio-visual understanding to enable video-heavy workflows common in real-world enterprise use. Today, we’re
X post · Local & open models★ Pick· ♥ 531
A YouTube tutorial companion repo where Muse Glimmer 30B (a 2-bit GGUF, run offline) looks at screenshots of badly designed web pages and generates and self-repairs modern replacement code.
GitHub · Local & open models
Ran Muse Glimmer 30B locally in the browser with custom WebGPU kernels at ~25 tok/s on an M4 Max, matching llama.cpp speed.
Reddit post · Local & open models★ Pick
AshutoshShrivastava
@ai_for_success
Got Muse Glimmer 30B running locally on my Mac (M5 Max - 128 GB )and the instruction following is impressive. need to setup with opencode next Video Speed - 5X
X post · Local & open models· ♥ 125
Sumanth
@Sumanth_077
Run and fine-tune Meta's Muse Glimmer locally! Meta released Muse Glimmer, a 30B dense vision model designed for local agentic and coding workflows. The first open model from Meta Superintelligence Labs, released under Apache 2.0. The model runs locally at different memory
X post · Local & open models· ♥ 26
LMSYS Org
@lmsysorg
@AIatMeta's Muse Glimmer (30B dense, open-weights) launches with SGLang day-0 support. We got ~230 tok/s on a single RTX 5090, with NVFP4 + DFlash on. It also works out of the box on @NVIDIAAIDev RTX Pro 6000, DGX Spark, and MLX for Mac. Speed and reliability have always been
X post · Local & open models· ♥ 129
A video companion repo with the commands and configs for running Muse Glimmer 30B locally on an RTX 3090 and comparing the Prime Agent and Hermes harnesses under the same conditions.
GitHub · Local & open models· ★ 1
Unsloth AI
@UnslothAI
Meta releases Muse Glimmer, a new 30B open model that runs on 18GB RAM. Muse Glimmer is Apache 2.0 licensed, supports vision and is the strongest agentic model for its size. Run and train the model via Unsloth. GGUF: huggingface.co/unsloth/Muse-G… Guide: unsloth.ai/docs/models/mu…
X post · Local & open models★ Pick· ♥ 2.8K
AICodeKing reviews Muse Glimmer for local agent setups, finding it strong at tool calling, multi-step tasks and failure recovery but weaker on general benchmarks.
Video · Local & open models
Sam Witteveen covers Meta's open-weight Muse Glimmer 30B release, pointing to the research blog and the Hugging Face collection.
Video · Local & open models· ♥ 405
llama.cpp imatrix quantizations of Muse Glimmer 30B with image support via mmproj and MTP/DFlash notes.
Resource · Local & open models· ♥ 19
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merve
@mervenoyann
Muse Glimmer 30B is shipped with DFlash drafter which speeds-up generation 2-4x at little memory cost 🔥 we support this in llama.cpp and transformers, see below how it looks like in the wild (llama webui) ⤵️
X post · Local & open models· ♥ 131
Alok
@analogalok
The "I don't have enough VRAM" excuse just died. I’m running Meta’s new 30B Muse Glimmer Q6_K_XL with a massive 130k context window on just 26GB VRAM FREE compute on Kaggle. Kaggle provides you free 2x Nvidia T4 GPUs. 30 hours usage each week! Yesterday, I showed you the
X post · Local & open models· ♥ 170
Ben Burtenshaw
@ben_burtenshaw
Meta is back with Muse Glimmer: a 30B open-source multimodal model built for local, agentic use. HF is shipping day-0 support and I built a few demos to see what it can do. First: we gave Glimmer tools and asked it to quantize itself.
X post · Local & open models· ♥ 125
My fun weekend project was to try to make the new Muse Glimmer 30B work with a longer context, deciding to go for 512k first. I had expected the usual YaRN shenanigans and maybe a LoRA. I couldn't have been wrong more. Upon closer look, Glimmer turned out to be rather unusual architecturally. The thing that make long-context adaptations painful in other models, full attention layers with token position encoding, it simply not there. Instead, only 2048 tokens-wide SWA layers have RoPE, and full GQA attention layers have no position encoding at all. It appears the model is trained to work with long-distance token relationships inferred from the context and SWA layers. It's a rather bold architecture bet, but it seems Meta managed to pull it off. As a result, the model architecture appears to be uniquely suited for context extension by simple mechanical means. To change model context length from stock 128k to, say, 512k, you need only to change “max_position_embeddings” config setting from 131072 to 524288. What confuses other models, like Qwen3.5 family, Glimmer just takes into its stride. I spent close to 70h of compute on DGX Spark to test stock model with extended context on a
Reddit post · Local & open models★ Pick
Cloud Codes runs Unsloth's 2-bit dynamic quant of Muse Glimmer 30B in about 14 GB of laptop memory and tests 100+ autonomous tool calls.
Video · Local & open models· ♥ 217
NVIDIA hosts a Muse Glimmer 30B endpoint on build.nvidia.com with Python (OpenAI, LangChain), JavaScript and curl examples for the ~29.6B multimodal model with 131K context.
Site · Local & open models
turboderp's self-calibrated EXL3 quants of Muse Glimmer 30B down to 1.75 bits per weight, with calibration and eval traces.
Resource · Local & open models· ♥ 14
Fireship breaks down Meta's Muse Glimmer, a 30B-parameter agentic model released under Apache 2.0, in a short explainer.
Video · Local & open models★ Pick· ♥ 13.6K
A pinned llama.cpp recipe for Muse Glimmer 30B on one NVIDIA GB10 that verifies Meta's official GGUFs by SHA-256 and reproduces text, tool-call, coding, vision and throughput checks.
GitHub · Local & open models· ★ 1
Digital Spaceport reviews Muse Glimmer 30B on a 4x 3090 EPYC home server, calling it weaker than Qwen 3.6 27B overall but good at one specific thing.
Video · Local & open models★ Pick· ♥ 550
TimDarcet
@TimDarcet
Happy to release ✨ Muse Glimmer ✨ - level ~= Qwen 3.6-27B - Apache 2 - 30B dense - quantized to run in 17GB - quant + spec dec => 50 tok/s on macbook m5 max, interactive, smooth Enjoy!
X post · Local & open models· ♥ 74
Pre-exported ExecuTorch PTE artifacts of Muse Glimmer 30B from Meta, lowered and optimized for specific target backends.
Resource · Local & open models· ♥ 34
Alok
@analogalok
Muse Glimmer, A 30B parameter dense model swallowing a 130,000 token context window using only 19.3 GB of VRAM (extreme efficiency). No KV cache quantization required. I just benched the new Muse Glimmer 30B (dense) on a single RTX 4090. We are pulling 3,100+ t/s prefill and 75
X post · Local & open models· ♥ 392
DSpark drafter warm-started from Meta's DFlash assistant, adding a rank-256 Markov head and a confidence head (about 103M new parameters).
Resource · Local & open models· ♥ 8
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A llama.cpp container that serves Muse Glimmer 30B with vision and DFlash on an RTX 5090, reporting 98.8 tok/s single-stream and about 250 tok/s aggregate with 131k context.
GitHub · Local & open models
ROCmFP4 and ROCmFP8 builds of Muse Glimmer 30B and its drafter, targeted and tested on AMD Strix Halo (gfx1151).
Resource · Local & open models· ♥ 18
A starter script that downloads Muse Glimmer 30B, serves it with llama.cpp on an Apple Silicon Mac and connects it to OpenCode, with the server listening only on localhost.
GitHub · Local & open models
AI at Meta
@AIatMeta
Muse Glimmer can complete multi-step agentic tasks end-to-end from a single natural language prompt. In this demo, it autonomously discovers a local Home Assistant instance via network tool calls, queries device APIs, writes a responsive HTML/CSS/JS dashboard from scratch, and
X post · Local & open models· ♥ 426
ollama
@ollama
Using @AIatMeta's Muse Glimmer all locally to process personal monthly credit card statements. Your data belongs to you! Try different agent tasks using your favorite apps / harnesses with Ollama.
X post · Local & open models★ Pick· ♥ 345
A self-hosted llama.cpp serving stack that runs Muse-Glimmer-30B GGUF with DFlash2 speculative decoding on Kaggle's NVIDIA T4 x2, exposed through an authenticated OpenAI-compatible gateway.
GitHub · Local & open models
glimmer-cli is a local TypeScript CLI for Muse Glimmer 30B and Muse Spark 1.2 via Ollama, with stubbed tools and a reproducible tool-use eval harness.
GitHub · Local & open models
Hi all, Profile v2.2 is out. It's an open-source optimizer for inference servers. It computes your GPU's roofline ceiling, measures your live server against it, names the bottleneck, gives the flag. You apply. It re-measures. Every fix answers to a number. vLLM only today. More engines next. This release: core rule engine rewritten. Eight rules on a priority DAG with mutual exclusivity. Five alarms fire, four echoes are silenced, one true cause survives. Deterministic. AMD cards are now supported too. Tuning today is chaos: try a flag, wait, squint at a dashboard, repeat for days. Profile turns it into deterministic engineering: measure, fix, verify. Results in a few iterations. Mine took 4, ~30 minutes. My setup: RTX 5090, muse-glimmer 30B, SWE-Bench agents, no DFlash spec decoding. • 81 → 421 tok/s at 25k ctx • $3.41 → $0.65 per 1M output tok • TTFT 224ms (p95 500ms), TPOT 23ms at end of run • 4.72 → 1.08 J/tok https://preview.redd.it/4vazyxkcq6kh1.png?width=2248&format=png&auto=webp&s=77923a489b6f725240d23a7953150b5779260734 One iteration regressed hard: KV thrashing, TTFT 32.8s. Profile labeled it worse. Next fix recovered it. Regressions stay in the record. Watc
Reddit post · Local & open models
196 builds · page 1 of 5
About this shelf
This is one of the two largest shelves, with 196 entries on Muse Glimmer 30B, Meta's open-weight model released under Apache 2.0. Much of it is quants: Meta's own GGUF, Unsloth's Dynamic 2.0 builds, bartowski's imatrix GGUF, mlx-community's 4-bit MLX, and FP8, INT4 and NVFP4 builds from Red Hat AI and NVIDIA. Meta's DFlash drafter and community DFlash 2 drafters speed up decoding.
The hardware reports are the practical part. Alok ran Glimmer with 130K context on Kaggle's free dual T4s. Cloud Codes ran Unsloth's 2-bit quant in about 14GB of laptop memory and logged 100+ tool calls. Reddit users report speeds on RTX 5090s, AMD V620s and an RX 7600 XT, and one got it running in the browser over WebGPU.
There are honest reviews too. Digital Spaceport found it weaker than Qwen 3.6 27B overall on a 4x 3090 rig, and one Reddit post documents a max_tokens setting that made it look dumb. Speed numbers are as reported by each author and depend heavily on quant and runtime.
Frequently asked
The fastest route is Ollama with ollama run muse-glimmer, or LM Studio, which carried it at launch. llama.cpp, MLX, vLLM and SGLang also support it, and this shelf has setup notes for each.
Meta says it fits in under 20GB at 4-bit and targets 24–32GB devices. Unsloth lists a 17GB 4-bit build and 58GB for BF16, and community 2-bit quants run in about 14GB.
The weights are released under Apache 2.0 on Hugging Face. It is a ~30B dense model distilled from Muse Spark, with text and image input and a 131,072-token context.
DFlash is a speculative-decoding drafter Meta ships alongside Glimmer. Meta's model card reports 3.1x faster generation on an RTX 5090 and 1.8x on an M5 Max with it.
