Stop burning tokens and introducing multi-second cloud latency just to make structured routing decisions. laya-mlx is a native Apple MLX runtime for Laya typed decision models that clocks in at an astonishing 7–14 ms on an M3 Max. By stripping away autoregressive text generation and heavy PyTorch dependencies, it gives local AI workflows instant, deterministic decision-making entirely on-device.
Get up and running with laya-mlx in just a few lines of code:
pip install mlx laya-mlx
python
from laya_mlx import LayaDecisionModel
model = LayaDecisionModel.from_pretrained("mizore/laya-decision-v1")
context = "User requested database query optimization on production cluster."
decision = model.decide(
context=context,
schema=["escalate_to_dba", "run_auto_explain", "reject_request"]
)
print(f"Decision: {decision.action} (Latency: {decision.latency_ms:.2f}ms)")
Most modern agentic architectures overuse massive 7B+ LLMs for tasks that are fundamentally multi-class classifications or rigid tool routers. Invoking an LLM via cloud API adds 500–1500 ms of latency and burns cash; running a 7B model locally via Ollama or vLLM consumes gigabytes of VRAM and still takes hundreds of milliseconds.
laya-mlx is built for engineers building autonomous local agent systems, real-time code assistants, and edge devices where sub-20ms routing is mandatory. If you need reliable, typed branch logic without spinning up a heavy generative pipeline, this is the architecture to watch.
Are you optimizing your local AI infrastructure for speed and efficiency? Star the project on GitHub: mizorewww/laya-mlx.
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