Research: Hardware-Optimized Models (NUC/Edge/AI-Hat)

Last Updated: 2026-02-11 12:45 PST

🏗️ Hardware Targets

• Tier 1 (NUC 13 Pro): Heavy orchestration, local RAG, coding assistants.
• Tier 2 (Jetson / Pi5 + AI Hat): Real-time computer vision, sensor fusion, edge inference.
• Tier 3 (ESP32-S3 / PicoClaw): Micro-inference, state machines, basic NLP patterns.

🧠 Model Curation (Hugging Face / Ollama)

1. Coding & Technical (C# / C++ / ROS2)

• DeepSeek-Coder-V2 (Lite): High performance/RAM efficiency ratio. Optimized for VS Code integration.
• StarCoder2-7B/15B: Excellent for local autocompletion without cloud latency.
• CodeLlama-7B (Quantized): Reliable fallback for C++ and firmware logic.

2. General Reasoning & Business Planning

• Mistral-7B-v0.3: The "Gold Standard" for NUC-level performance. Highly balanced for planning and document analysis.
• Llama-3-8B (GGUF Quantized): Superior reasoning for "Business-in-a-Box" template generation.
• Phi-3-Mini (3.8B): High-speed "Sequential Flow" orchestration. Small enough for Pi5 + AI Hat.

3. Edge Vision & Robotics (Jetson / Pi5)

• YOLOv8/v10 (NCNN/TensorRT): Mandatory for Arrma Mojave real-time obstacle detection.
• Moondream2: Tiny vision-language model for "describing" what the RC car sees via the XIAO Sense stream.

📈 Investment & Strategic Plays

• Focus: Quantized models (GGUF/AWQ) that allow "Enterprise-Grade" reasoning on "Consumer-Grade" power envelopes.
• Goal: Build the "Isnad-Verified" model library for client deployment.