Run Laya on your Mac
Apple Silicon is the best-covered platform in the Laya ecosystem. Independent ports run the upstream checkpoints on the MLX GPU, through Core ML on the Neural Engine, or inside native Swift apps. Each port publishes its own speed and fidelity measurements, all taken on the author's own Mac.
308 projects
laya-mlx
mizorewww
Runs Laya typed-decision models locally on Apple Silicon using MLX
laya-coreml
mizorewww
Runs Laya typed-decision models locally on Apple Silicon using Core ML and the Neural Engine
laya-ultrafast
ipenywis
Runs a browser automation agent using local Laya decisions through MLX
FluidUse
FluidInference
Automates form filling on Apple devices with on-device models, including Laya through Core ML
keel
codejunkie99
A macOS coding workspace uses local Laya or hosted Jev to select task routes
laya-mlx
aac6fef
An MLX FP16 conversion of Laya for native inference on Apple silicon
laya-vs-jev
virajbhartiya
A side-by-side T-Rex game compares local Laya and hosted Jev decision models
laya.cpp
lkarlslund
A native C++ runtime runs Laya typed-decision models across GPU and Apple backends
system-one-foundation-models
peterfriese
Bridges Laya and Jev System One decision models into Apple’s Foundation Models framework
laya-coreml
FluidInference
A Core ML conversion of Laya Multilingual for inference on Apple devices
sys1
alvarobartt
Serves Laya decision models through a Rust System One API with CPU, CUDA, and Metal support
arbiter
0xBakeer
Serves Laya and other typed-decision models locally with a Jev-compatible API
eikos
caiovicentino
Builds calibrated typed-decision models for finance and trading and evaluates them against Laya
laya-multilingual-mlx
aac6fef
Converts the multilingual Laya decision model to MLX for Apple silicon
laya-multilingual-coreml-ane
aac6fef
Packages the multilingual Laya decision model as a Core ML bundle for Apple devices
MacJev-322M-4K-Laya
chaoliangUNSW
Fine-tunes Laya for long-context local Mac agents and compares it with the base model
laya-jev-lab
yibie
Compares Jev and Laya decision models and evaluates a local-first inference cascade
LayaStudio
biplovgautam
Fine-tunes Laya decision models locally on Mac and evaluates results before deployment
opendecider
manjunathshiva
Provides calibrated System 1 decision models and benchmarks them against Laya and other models
fast-jev-compaction-laya
kaiyes
Compacts OpenCode context using decisions from a locally served Laya model
laya-apple
tc3oliver
Runs Laya locally on Apple Silicon with MLX GPU and Apple Neural Engine support
laya-coreml
aac6fef
Packages the Laya decision model as a Core ML bundle for Apple devices
laya-as-judge
rbrus
Uses Laya-MLX to evaluate models and agents with typed-decision judgments
kime
tamnd
A Rust inference engine and HTTP server runs Laya checkpoints across CPU, CUDA, and Apple GPUs
Pick a runtime
MLX (Python). laya-mlx is the most widely used port. It needs Apple Silicon, Python 3.11+ and macOS 14+:
pip install laya-mlx
import laya_mlx as laya
agent = laya.load("aac6fef/laya-mlx")
result = agent.predict(
"I was billed twice. Please refund the duplicate.",
{"department": {"type": "choice",
"instructions": "Who should handle this?",
"criteria": ["billing", "technical", "sales"]}},
)
print(result["answers"]["department"])
Its README reports 13.42 ms P50 for one short question with the 421M English checkpoint and 7.39 ms with the 322M multilingual one, FP16 on an M3 Max.
Core ML and the Neural Engine. laya-coreml needs macOS 15+ and Python 3.11 to 3.13 (pip install laya-coreml). Its README reports 4.98 ms P50 for one short multilingual decision on the M3 Max Neural Engine. laya-apple runs the MLX GPU and the Neural Engine together and serves a Jev-compatible API:
pip install 'laya-apple[serve,ane]'
laya-apple serve # http://127.0.0.1:8642
Swift. laya-mlx-swift embeds Laya in a macOS app with MLX (about 804 MiB download). LayaKit is a Swift package over the laya-coreml bundles, and FluidUse ships a LayaManager for Core ML.
PyTorch MPS. The official laya package also runs on the Mac GPU; laya-mps wraps it in a local server with low-memory modes.
Caveats
- All of these are independent ports, not official Convai Innovations releases. Most publish fidelity checks against upstream answers; read them.
- The Neural Engine bundle in laya-coreml has a 96-token total limit covering question, options and state. Use the 1024-token general bundle for longer input.
- Latency numbers are single-machine measurements. Your Mac, input length and question count will change them.
More ways to use Laya