Upload your model. Define your hardware constraints. Get back a production-ready, optimized model with benchmarks — in minutes, not weeks.
EdgeForge automates the entire model optimization pipeline — from analysis to export — so you can focus on building products, not compressing models.
Drag and drop a PyTorch checkpoint, ONNX file, or just paste a HuggingFace model ID. We handle the rest.
Select from 10+ pre-built device profiles — Raspberry Pi, Android, Jetson, iOS, browser — or define custom hardware constraints.
Get your optimized model with a full benchmark report: size, latency, accuracy tradeoffs — ready for production deployment.
Every optimization technique in EdgeForge is designed for real-world deployment where compute is scarce and connectivity isn't guaranteed.
INT8, INT4, and per-layer mixed-precision. Including GPTQ/AWQ for LLMs. Automatic sensitivity analysis picks the right strategy.
Remove entire channels and attention heads for real speedup on any hardware — no sparse runtime needed.
Proprietary technique using optimal transport theory. 10–15% better accuracy retention than standard knowledge distillation.
Every job produces a detailed comparison: size, latency (p50/p95/p99), accuracy, and resource usage on your target hardware.
Pre-built targets for Android, Raspberry Pi, Jetson, iOS, browser (WASM), and TinyML. Create custom profiles in seconds.
Integrate optimization into your CI/CD pipeline. Programmatic access to everything — upload, optimize, benchmark, download.
Real optimizations on popular models targeting common edge devices. No cherry-picked results.
Pre-built profiles for the most popular edge targets. Custom profiles for everything else.
No credit card required. Upgrade when you need more power.
Join the private beta. Be among the first to optimize your models with EdgeForge.
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