Muse Glimmer (30B dense VLM)¶

Muse Glimmer is Meta Superintelligence Lab's 29.8B-parameter dense multimodal model, built for agentic work on consumer hardware. Architecturally it is Gemma2-style sandwich norms plus gated attention (a sigmoid output gate alongside Q/K/V), a 3:1 sliding(2048)/full attention pattern with NoPE on the full-attention layers, final logit softcapping, and a 1.9B ViT-G/14 perception encoder.

This is the one case where TurboQuant beats MLX's affine quantizer outright on quality, at matched bit width — and does it while quantizing more of the model:

800-token fixed-passage perplexity, greedy, M4 Max. The affine build is 2.4–2.8× faster to decode — the usual codebook-vs-affine trade. Do not read the tq3 row as a TurboQuant deficiency: the gap there is the 3-bit step, which is exactly what the matched-4-bit control isolates.

Most of the affine build's extra 5 GB is the embedding and the vision tower, which mlx-vlm declines to quantize. embed_tokens is a NormedEmbedding whose inherited to_quantized would silently drop its RMS normalization; TurboQuant quantizes it through a norm-preserving subclass instead.

# mlx-vlm 0.6.12 shipped the Muse Glimmer classes, so this is a plain install.
pip install "turboquant-mlx-full[vlm]"

python -m turboquant_mlx.convert_vlm \
  --hf-path meta-models/Muse-Glimmer-30B \
  --mlx-path ./Muse-Glimmer-30B-tq4-g64 \
  --bits 4 --group-size 64 --quantize-extras --extras-bits 4

python -m turboquant_mlx.generate_vlm \
  --model ./Muse-Glimmer-30B-tq4-g64 --prompt "..." --max-tokens 256

--extras-bits 4 matters here: the embedding and vision tower are 3.3B parameters between them, ~3.9 GB at the 8-bit default versus ~1.8 GB at 4-bit.

16 GB Mac mini: yes — measured. tq3-g64 runs resident on a Mac16,10 (macOS 26.5.2) with iogpu.wired_limit_mb=14336, at every prompt length tried up to 5068 tokens:

prompt	peak	prefill	decode
268 tok	13.44 GiB	19.9 tok/s	3.70 tok/s
868 tok	14.13 GiB	20.2 tok/s	3.44 tok/s
2068 tok	13.91 GiB	17.0 tok/s	3.38 tok/s
5068 tok	14.21 GiB	6.0 tok/s	3.56 tok/s

Meta's own smallest Apple Silicon artifact is 17.95 GB, text-only and without the drafter — larger than the machine's whole RAM. Two caveats: headroom at 5068 tokens is 0.20 GB, and prefill collapses past ~2000 tokens (20 → 6 tok/s) while decode stays flat, so long prompts are batch work. The smaller alternative does not work — a tq3a-tq2e-g64 hybrid fits at 9.63 GiB but measures PPL 7.5547 (vs 5.0454) and visibly corrupts text, the same way 2-bit experts failed on 32-expert GPT-OSS-20B. 2-bit on a dense MLP is not a usable tier.

Muse Glimmer also ships a DFlash block-diffusion drafter (5.11 GB, 5 layers, block 16, tapping target layers {1,13,25,37,49}). It is not wired up here. Worth noting it is a dense target, which is the regime where DFlash speculation paid off in earlier testing rather than the MoE regime where it did not.