Deploying machine learning models in the cloud can result in high API bills and slow network latency. Ananta Labs provides expert edge AI inference engineering, compiling and optimizing neural networks to run locally on low-power devices, desktop applications, and browsers.
Our Edge Optimizations
We reduce model footprint while maintaining accuracy using advanced compilation methods:
- Model Quantization: Converting model weights from FP32 to INT8 or FP16, resulting in a 4x reduction in size with negligible loss in accuracy.
- ONNX & TensorRT Compilation: Building hardware-specific runtimes optimized for NVIDIA GPUs, Intel CPUs, or Apple Neural Engines.
- WebAssembly (Wasm) & WebGL: Serving light computer vision and text models directly inside standard web browsers for instant interaction.
- Embedded Device Optimization: Compiling models for Raspberry Pi, Jetson Nano, and specialized edge microcontroller units (MCUs).
The Advantages of Edge Deployments
Local inference guarantees 100% privacy, runs without an active internet connection, and eliminates recurring cloud server fees. This makes it the ideal choice for digital signature capture, offline machinery analytics, and mobile applications.
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