LTX-2.3-22B — 24GB (MLX)

Mixed-precision quantized version of Lightricks/LTX-2.3 optimised by baa.ai using a proprietary Black Sheep AI method.

Per-tensor bit-width allocation via advanced sensitivity analysis. Runs natively on Apple Silicon via ltx-2-mlx.

Metrics

Metric	Value
Transformer size	19.8 GB
Total model size	~28 GB
Average bits (transformer)	7.9
Quantized layers	1,157 / 1,760

Bit distribution:

Bits	Layers
5	3
6	75
8	1,079

Requirements

Apple Silicon (M1 or later), macOS 13+
~26 GB unified memory
Python 3.10+

pip install mlx flask ltx-pipelines-mlx ltx-core-mlx

Usage — Web App

git clone https://huggingface.co/baa-ai/LTX-2.3-22B-RAM-24GB-MLX
cd LTX-2.3-22B-RAM-24GB-MLX
python webapp.py

# Side-by-side comparison with the 12GB version:
git clone https://huggingface.co/baa-ai/LTX-2.3-22B-RAM-12GB-MLX ../LTX-2.3-22B-RAM-12GB-MLX
python webapp.py --compare-dir ../LTX-2.3-22B-RAM-12GB-MLX

Open http://localhost:7860 — enter a prompt, set resolution and duration, and hit Generate. The live log streams generation progress; the video plays in the browser when done.

Usage — CLI

python generate.py \
    --model-dir /path/to/LTX-2.3-22B-RAM-24GB-MLX \
    --prompt "A serene mountain lake at sunrise, mist over calm water, pine trees reflected" \
    --height 480 --width 704 --num-frames 65 \
    --output output.mp4

Frame count must follow 32k + 1 (33, 65, 97, 129 …). Duration in seconds = frames ÷ fps.

Model Comparison

Model	Transformer	Avg bpw	Peak RAM
LTX-2.3-22B-RAM-12GB-MLX	9.3 GB	4.6	~14 GB
LTX-2.3-22B-RAM-24GB-MLX	19.8 GB	7.9	~26 GB
dgrauet/ltx-2.3-mlx-q8	19.2 GB	8.0 uniform	~26 GB

Quantized by baa.ai

Black Sheep AI Products

Black Sheep AI builds private AI with the Bounded AI Architecture (BAA): three layers, each bound to something you control, on infrastructure you control. Nothing leaves your boundary.

Ram (Reasoning Layer) — Full-capability frontier models, shrunk to fit smaller GPUs with reasoning kept and certified. They rely strictly on your Data Modules for knowledge, so you swap the model as better ones ship.

Paddock (Knowledge Plane) — Binds your Data Modules directly to the reasoning layer: one per topic, period, person, permission or tenant. Permission, freeze, hand back or delete each one on its own.

Watchman (GRC Layer) — Governance, risk and compliance in a single, complete audit log: every module permission, every bind, every model's provenance. Five certificates before anything goes live.

Shepherd — Takes a frontier model, compresses it into a Ram reasoning model, certifies it with Watchman, binds it to your Paddock, and deploys the Bounded AI Architecture to your infrastructure: your cloud, a Mac Studio, or an air-gapped facility.

Learn more at baa.ai — Private AI for enterprises whose data cannot leave the building.