FOSS-2026/EdgeForge

EdgeForge is an open-source, offline-first toolkit that helps run AI models on low-resource devices like laptops, Raspberry Pi, and older PCs. It profiles hardware, evaluates model feasibility, suggests optimizations, and generates ready-to-run offline deployment packages with clear guidance. # 🧠 EdgeForge — AI on Any Device **Bring powerful AI to low-compute devices — Offline, Open, and Easy.** EdgeForge is an open-source, offline-first toolkit that helps developers and makers run AI models efficiently on low-resource and legacy devices such as laptops, Raspberry Pi, older PCs, and edge servers — without cloud dependency. --- ## 🌍 Why EdgeForge? Modern AI tooling often assumes: - Cloud GPUs - Fast, reliable internet - Expensive hardware In real-world environments — rural regions, classrooms, research labs, NGOs, and privacy-sensitive settings — these assumptions break down. **EdgeForge bridges this gap** by making AI practical, local, and dependable on the hardware people already have. --- ## 🧠 What EdgeForge Does EdgeForge answers one simple but critical question: > **“Will this AI model run on my device — and how?”** It does this by: - Profiling real hardware constraints - Analyzing AI model feasibility - Suggesting explainable optimization strategies - Visualizing performance tradeoffs - Generating ready-to-run offline deployment bundles --- ## ✨ Key Features ### 🔍 Device Profiler - Detects CPU, RAM, SIMD, GPU/NPU - Benchmarks device limits - Estimates safe model size and throughput ### 📦 Model Intake & Analysis - Supports **ONNX** and **GGUF** - Extracts model metadata - Performs feasibility checks ### 🧠 AI-Guided Optimization Planner - Suggests quantization (INT8, INT4) - Recommends runtime backends - Explains tradeoffs with confidence scores ### 📊 Visual Dashboard - Device capability overview - Accuracy vs latency insights - Clear **“Can it run?”** indicator ### 📦 Offline Deployment Packager - Optimized model artifacts - Prebuilt runtime binaries - Shell scripts and configs - **No internet required after download** --- ## ⚠️ Limitations - Does not train or fine-tune models - Initial support limited to ONNX and GGUF - Performance estimates are predictive, not guaranteed - Depends on underlying runtime support These tradeoffs …