Sub-5ms Decision Inference on Apple Silicon: Inside laya-coreml

Running classification, routing, and structured decision trees directly on edge hardware often hits a wall: Python runtimes add latency overhead, and massive LLMs are overkill for low-latency triage. laya-coreml bridges that gap by compiling Laya typed decisions to Apple Core ML, leveraging the Apple Neural Engine (ANE) to hit ~5 ms decision latencies on M-series chips.

TL;DR

laya-coreml enables fully local, typed decision model inference on Apple Silicon via Core ML and the Apple Neural Engine. It eliminates cloud API costs and network roundtrips by delivering validated sub-5ms decision paths on an M3 Max with reproducible energy efficiency benchmarks. It is a drop-in solution for edge routing, agentic guardrails, and real-time on-device classification.

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Key Features & Benchmarks

• Sub-5ms Decision Paths: Validated inference times as low as ~5 ms on Apple M3 Max, fully offloading execution to the Apple Neural Engine (ANE).
• Strictly Typed Outputs: Built around Laya's typed decision framework, ensuring structured, deterministic schema outputs rather than unpredictable free-form text.
• Energy-Efficient Execution: Hardware-accelerated ANE offloading slashes battery consumption compared to running quantization frameworks (like GGUF/llama.cpp) across raw GPU/CPU cores.
• Reproducible Performance: Includes native benchmark harnesses to measure memory pressure, token-to-decision throughput, and wattage consumption across M1 through M3 architectures.
• Zero External Dependencies at Runtime: Compiles down to standard .mlpackage artifacts ready for native Swift, Objective-C, or Python Core ML bindings.

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Quick Start

Clone the repository and run the validation pipeline directly using Python:

# Clone the repo
git clone https://github.com/mizorewww/laya-coreml.git
cd laya-coreml

# Install required dependencies
pip install -r requirements.txt
coremltools

# Run the standard validation benchmark on your Mac
python benchmark.py --model laya-decision-v1 --device ane

To integrate into your Python pipeline:

import coremltools as ct
from PIL import Image
import numpy as np

# Load the compiled ANE-optimized decision model
model = ct.models.MLModel("models/LayaDecision.mlpackage")

# Execute a typed decision run
inputs = {"feature_vector": np.random.randn(1, 512).astype(np.float32)}
prediction = model.predict(inputs)

print("Predicted Routing Target:", prediction["decision_label"])

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Why It Matters

If you are building autonomous local agents, audio/video pipelines, or on-device filtering for macOS/iOS apps, passing every intermediate step through a 7B parameter LLM is slow, hot, and battery-draining.

laya-coreml lets you execute fast, typed classification and semantic decisions in parallel on the ANE while your primary GPU remains free for heavy rendering or generation tasks. It is ideal for:

• Client-Side Agent Guardrails: Validate user prompts and tool actions before execution.
• Low-Latency Router Nodes: Direct queries locally to cloud APIs or smaller local SLMs depending on complexity.
• Energy-Conscious Mobile Devs: Build responsive macOS and iOS utilities without spinning up fans or depleting user batteries.

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