mlx-community/Qwen3.5-122B-A10B-OptiQ-2bit

Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon, no PyTorch and no cloud. Read the write-up · All OptiQ quants · Docs

A 122-billion-parameter model that runs on a 36 GB Mac. This is a 2-bit mixed-precision MLX quant of Qwen3.5-122B-A10B (244 GB at bf16), produced by mlx-optiq. It is 44 GB on disk. While it generates, only ~12 GB sits in RAM: the attention, router and embeddings stay resident, and the 35 GB of mixture-of-experts weights stream off the SSD one expert at a time through optiq serve --stream-experts.

Image input

It takes images. The SigLIP vision tower rides at bf16 in a sidecar (optiq/optiq_vision.safetensors, 0.90 GB), so the image path keeps full precision while the language tower runs at 2 bits. The tower stays resident; only the experts stream.

Verified on a 36 GB M3 Max with expert streaming on, 11.6 GB resident:

image	output
a red circle on white	"The image contains a red circle."
a blue square on white	"The image contains a blue square."

from PIL import Image
from optiq.runtime.moe_stream import load_streaming
from optiq.runtime.engine import OptiqEngine

model, tok = load_streaming("mlx-community/Qwen3.5-122B-A10B-OptiQ-2bit")
eng = OptiqEngine.from_loaded(model, tok, "mlx-community/Qwen3.5-122B-A10B-OptiQ-2bit")
print(eng.generate("What is in this image?", images=[Image.open("photo.jpg")]).text)

Requires mlx-optiq >= 0.3.3.

Asked to write Flappy Bird in a single HTML file, the 2-bit model produced a complete, working game. Here it is playing it:

What it is

Property	Value
Base	Qwen3.5-122B-A10B (122 B total, ~10 B active per token, 256 experts/layer)
Method	OptiQ static — structural per-layer bit allocation, no calibration
Bit-widths	4-bit on attention / router / embeddings / first+last block, 2-bit on the routed experts
Achieved bits-per-weight	2.50
On disk	44 GB
Resident while running	~12 GB (experts streamed)
Decode speed	~5 tok/s on an M3 Max (36 GB)

The allocation is rule-based: for a 122 B MoE, exact calibration-driven sensitivity would run for days and needs the full model resident as a reference, so OptiQ's static method assigns bits from architecture alone. On small models it matches the calibration method at a fraction of the cost (see the methods comparison).

Run it

This is a Qwen3.5 MoE (model_type: qwen3_5_moe), so it needs mlx-lm from main and import optiq (the MoE text tower postdates the 0.31.3 PyPI release; the main build also reports 0.31.3, so install from git, not a version pin):

pip install -U mlx-optiq "mlx-lm @ git+https://github.com/ml-explore/mlx-lm.git"

Serve it with SSD expert streaming (auto-enabled for a MoE too big to fit resident; --stream-experts forces it):

optiq serve --model mlx-community/Qwen3.5-122B-A10B-OptiQ-2bit --stream-experts

Then open the Lab, ask for a game, and watch it render in the Canvas pane. Streaming keeps the residency flat (~12 GB) no matter how large the model on disk is, at the cost of per-token expert reads (decode is I/O-bound).

Notes

This is an extreme quant. 2-bit on the experts is lossy, and the point of this artifact is that a 122 B model runs at all on consumer Apple Silicon, with coherent output. For reference quality on this base, use a higher-bit quant (Qwen3.5-122B-A10B-4bit and up). The full story is in the blog post.