1 Day in and I feel okay saying Muse-Glimmer-30B finally beats 3.6-27B for the size in some use-cases

Reddit r/LocalLLaMA Models
model-comparison efficiency quantization coding open-source

Summary

A user reports that Muse-Glimmer-30B outperforms Qwen3.6-27B in reasoning efficiency and trivia knowledge, while being somewhat weaker at coding, making it a viable option on 24GB GPUs.

A few things right off the bat: it reasons very efficiently. Like Grok 4.5 levels of efficient thinking it quantizes very well. My first few tests with iq3_xxs were better than Qwen/Gemma behaved at that size its knowledge depth is amazing. It beats Qwen3.6 27B on no-tools trivia. in OpenCode it is a much more efficient agent than 27B. Both models accomplish their tasks but Muse-Glimmer got there faster every time I'll say that it's worse at most things coding, probably being closer to Gemma4-31B level.. but damn there's a lot of places where I'd use this model on a 24GB GPU right now and it's been a while since anything has filled that spot except for 3.6-27B
Original Article

Similar Articles

Muse Glimmer ACTUALLY fits on a single RTX 3090

Reddit r/LocalLLaMA

User reports that Muse Glimmer, a 30B model, fits on a single RTX 3090 with full 256k context using Q4_K_XL quantization and DFlash, achieving 64-124 tok/s and perfect long-context retrieval, unlike comparable models.

Tested in Coding: BF16 Muse Glimmer vs BF16 Qwen3.6 27B

Reddit r/LocalLLaMA

A hands-on coding comparison between BF16 Muse Glimmer and BF16 Qwen3.6 27B, evaluating diagnostic quality, implementation reliability, and self-correction on a complex enterprise web app. Qwen shows better persistence on stubborn bugs, while Muse Glimmer struggles with iterative fixes.

Layman's comparison on Qwen3.6 35b-a3b and Gemma4 26b-a4b-it

Reddit r/LocalLLaMA

A user compares Qwen3.6 35B-A3B and Gemma 4 26B-A4B-IT running locally on a 16GB VRAM GPU via LM Studio, finding Qwen3.6 produces more detailed outputs while both run at comparable speeds. The post is an informal community comparison using quantized models.