Muse Glimmer 30B
2026-08-10
Meta released the weights for its Muse Glimmer model, following up on Muse Spark 1.2. One of my hobbies these days is trying out different models and wasting electricity and a few brain cells on them.
The server configuration below follows Meta's Muse Glimmer 30B model card.
llama serve \
-hf meta-models/Muse-Glimmer-30B-GGUF:17gb \
--spec-type draft-dflash \
--spec-draft-n-max 15 \
-ngl all -ngld all \
-c 131072 -np 4 \
-fa on --fit on --jinja \
--temp 1.0 --top-p 0.95 --top-k 64 --min-p 0 \
--host 0.0.0.0 --alias muse-glimmer --kv-unified --reasoning-preserve
llama serve \
-hf meta-models/Muse-Glimmer-30B-GGUF:dynamic \
--spec-type draft-dflash \
--spec-draft-n-max 15 \
-ngl all -ngld all \
-c 131072 -np 4 \
-fa on --fit on --jinja \
--temp 1.0 --top-p 0.95 --top-k 64 --min-p 0 \
--host 0.0.0.0 --alias muse-glimmer --kv-unified --reasoning-preserve
Performance
17gb: 57.95 tokens/s, using about 20 GB of VRAM with a 128K-token context.dynamic: 56.84 tokens/s, using about 23 GB of VRAM with a 128K-token context.
In initial testing, Muse Glimmer is noticeably faster than Gemma 4, uses less memory, and comfortably handles four agents in parallel. I also did not notice much difference between the 17gb and dynamic variants. The former may be slightly faster, but I could not tell in practice.
I will try Muse Glimmer for a few days to see whether it can replace Gemma 4 and Qwen 3.6 in my workflow. For reference, here is my current Gemma 4 configuration:
llama serve \
-hf ggml-org/gemma-4-31B-it-GGUF:Q4_0 \
--spec-type draft-mtp \
--spec-draft-n-max 4 \
-ngl all -ngld all \
-c 65536 -np 2 \
-fa on --fit on --jinja \
--temp 1.0 --top-k 64 \
--reasoning on \
--chat-template-kwargs '{"preserve_thinking":true}' \
--host 0.0.0.0 \
--port 8080 \
--alias gemma4 \
--kv-unified