---

license: apache-2.0

base_model: meta-models/Muse-Glimmer-30B

base_model_relation: quantized

library_name: llama.cpp

pipeline_tag: text-generation

tags:

- gguf

- sycl

- intel-arc

- muse-glimmer

- llama.cpp

---

<p align="center">

<img src="muse-b70-hero.png" alt="Muse B70" width="100%">

</p>

muse

Muse Glimmer 30B (Q4_K_M, 15.6 GB) on Intel Arc Pro B70. One seat, full 131k context.

Dense 28B

Kernels and binaries: newjordan/museB70

| | |

|---|---|

| decode @ 129k cached | 19.0 t/s |

| full-ctx prime | 503 t/s |

| short decode / prefill | 28.6 / ~1277 t/s |

Install

git clone https://github.com/newjordan/museB70.git
cd museB70

hf download Frosty40/Muse-Glimmer-30B-ArcB70-GGUF muse-glimmer-30B-kquant-17gb.gguf

gh release download v2026.08.12-b70 --repo newjordan/museB70 \
  --pattern 'muse-serve-3ce44d373-linux-b70.tar.zst'
sha256sum -c releases/ASSET_SHA256SUMS
tar --zstd -C releases -xf muse-serve-3ce44d373-linux-b70.tar.zst
rm -f muse-serve-3ce44d373-linux-b70.tar.zst

Serve

MODEL=muse-glimmer-30B-kquant-17gb.gguf \
LLAMA_BIN=./releases/muse-serve-3ce44d373/bin/llama-server \
  ./serve-muse-arc.sh

curl -s localhost:8095/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"muse-glimmer-30b-q4","messages":[{"role":"user","content":"hi"}],"max_tokens":128}'

Muse thinks in reasoning_content. Echo both that and content on later turns, and give it enough max_tokens.

---

This is a serving configuration for the Intel Arc Pro B70 — not a new model.

Model and official kquant: meta-models/Muse-Glimmer-30B (GGUF), Apache-2.0, © Meta. Runtime: llama.cpp SYCL + Intel oneAPI. Serving config and B70 kernels: newjordan/museB70.