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ArmSight

by 0xConsole
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Python
Hybrid

ArmSight

Autonomous AI agent for Arm64 ML model inference optimization. Analyzes ONNX models, applies REAL INT8 quantization, and generates Arm64-optimized deployment packages — all through an MCP-compatible tool interface that an AI agent can call autonomously.

Built for the Arm Create: AI Optimization Challenge (Cloud AI track).

🎯 The Problem

Deploying ML models on Arm64 (AWS Graviton, Cortex-A, Neoverse) requires platform-specific knowledge: which quantization scheme to use, how to tune thread parallelism for multi-core, when to leverage NEON SIMD, and how to package everything into an Arm64-optimized container. Most developers ship unoptimized FP32 models and leave significant performance on the table.

Related MCP server: LUMINO MCP Server

💡 The Solution

ArmSight is an autonomous AI agent that:

  1. Analyzes any ONNX model — operators, layers, precision, parameter count, input/output shapes

  2. Recommends Arm64-specific optimizations — INT8 quantization, NEON SIMD fusion, thread parallelism, memory layout, ACL provider

  3. Applies real INT8 dynamic quantization using onnxruntime.quantization — producing measurably smaller models (typically ~4x size reduction)

  4. Benchmarks before/after — real inference latency, throughput, and speedup measurements

  5. Generates a complete Arm64-optimized deployment package — Dockerfile (linux/arm64), FastAPI inference server, benchmark script

🏆 Unique Angle

Unlike generic model optimizers, ArmSight exposes its capabilities as MCP (Model Context Protocol) tools that an AI agent can call autonomously — analyze_model, optimize_model, benchmark_model, recommend_optimizations, generate_deployment, full_pipeline. This makes ArmSight not just a tool, but an agent-native optimization platform.

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                        Web UI (HTML/CSS/JS)                  │
│  Upload ONNX → Analyze → Recommend → Quantize → Benchmark    │
└──────────────────────────┬──────────────────────────────────┘
                           │ HTTP
┌──────────────────────────▼──────────────────────────────────┐
│                    FastAPI Backend (Python)                 │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌─────────────┐  │
│  │ Analyzer │  │Quantizer │  │  Recomm. │  │  Deployment │  │
│  │ (onnx)   │  │(onnxrt)  │  │  Engine  │  │  Generator  │  │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘  └──────┬──────┘  │
│       └──────────┬───┴─────────────┴───────────────┘         │
│                  ▼                                           │
│         ┌──────────────────┐                                 │
│         │   MCP Server     │ ← AI agent calls these tools     │
│         │  (tool registry) │   autonomously via MCP protocol  │
│         └──────────────────┘                                 │
└──────────────────────────────────────────────────────────────┘
                           │
┌──────────────────────────▼──────────────────────────────────┐
│                    Vercel (Serverless)                       │
│         FastAPI on Python runtime — free tier               │
└─────────────────────────────────────────────────────────────┘

⚡ Quick Start

Prerequisites

  • Python 3.9+

  • An ONNX model file (or use the built-in example model generator)

Setup (< 5 commands)

# 1. Clone
git clone https://github.com/0xConsole/arm-sight-agent.git
cd arm-sight-agent

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run locally
uvicorn app.main:app --reload --port 8000

# 4. Open the UI
open http://localhost:8000

Use via API / MCP

# List MCP tools (what an AI agent sees)
curl http://localhost:8000/mcp/tools | python -m json.tool

# Call the full pipeline autonomously (analyze → quantize → benchmark → deploy)
curl -X POST http://localhost:8000/mcp/call \
  -H "Content-Type: application/json" \
  -d '{"name": "analyze_model", "arguments": {"model_path": "examples/example_model.onnx"}}'

🛠️ Tech Stack

Component

Technology

Backend

Python + FastAPI

Model analysis

onnx + onnxruntime

Quantization

onnxruntime.quantization.quantize_dynamic (REAL INT8)

Agent interface

MCP (Model Context Protocol) tool pattern

Frontend

Vanilla HTML/CSS/JS (no framework)

Deployment

Vercel serverless (Python runtime)

Target platform

linux/arm64 (AWS Graviton, Cortex-A, Neoverse)

✅ What's Real vs. Mocked

Feature

Status

Notes

ONNX model analysis

✅ REAL

Uses onnx + onnxruntime to parse graph, count operators/parameters

INT8 quantization

✅ REAL

onnxruntime.quantization.quantize_dynamic — produces genuinely smaller ONNX files

Size measurement

✅ REAL

Byte-level before/after file size comparison

Inference benchmarking

✅ REAL

Actual session.run() timing on CPU (mean/p50/p95 latency, throughput)

Arm64 recommendations

✅ REAL

Based on actual model architecture (operators, precision, param count)

Deployment package

✅ REAL

Generates working Dockerfile targeting linux/arm64 + FastAPI server + benchmark script

MCP tool interface

✅ REAL

Tools are callable via POST /mcp/call — any MCP client can invoke them

Nothing is mocked. Every measurement comes from real ONNX runtime operations.

📊 Measurable Improvements (Example)

For a typical FP32 ONNX model:

Metric

Before (FP32)

After (INT8)

Improvement

Model size

~4.2 MB

~1.1 MB

4.0x reduction

Inference latency

~2.5 ms

~1.8 ms

~28% faster

Throughput

~400 ops/s

~550 ops/s

~37% higher

Actual numbers vary by model. The quantization and benchmarking are real — run it on your model to see your results.

🐳 Generated Deployment Package

The generate_deployment tool produces:

deploy_package/
├── Dockerfile          # linux/arm64 target, ONNX Runtime with NEON
├── server.py           # FastAPI inference server (optimized session options)
├── model.onnx          # Your (optionally quantized) model
├── benchmark.py        # Latency/throughput benchmark script
├── docker-compose.yml  # One-command deployment
└── README.md           # Usage instructions
# Build and run on Arm64
docker buildx build --platform linux/arm64 -t armsight-inference .
docker run --rm -p 8000:8000 armsight-inference
python benchmark.py http://localhost:8000

🔌 MCP Tool Reference

ArmSight exposes 6 tools via the MCP interface:

Tool

Description

analyze_model

Analyze ONNX architecture: operators, precision, params

optimize_model

Apply INT8 dynamic quantization (real size reduction)

benchmark_model

Measure inference latency and throughput

recommend_optimizations

Generate Arm64-specific recommendations

generate_deployment

Create Arm64 Docker + FastAPI deployment package

full_pipeline

Run all of the above autonomously

📁 Project Structure

arm-sight-agent/
├── api/
│   └── index.py          # Vercel serverless entry point
├── app/
│   ├── main.py           # FastAPI app + routes
│   ├── analyzer.py       # ONNX model analysis
│   ├── quantizer.py      # INT8 quantization (REAL)
│   ├── recommendations.py # Arm64 optimization recommendations
│   ├── deployment.py     # Deployment package generator
│   └── mcp_server.py     # MCP tool registry + dispatch
├── static/
│   └── index.html        # Web UI
├── requirements.txt
├── vercel.json
└── README.md

📜 License

Apache License 2.0 — see LICENSE.

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