On-Device & Edge AI

Small Language Models (SLMs) under 3B parameters run efficiently on modern devices — we specialize in Qwen3, Phi-3, Llama 3.2, and custom distilled models. For vision tasks, optimized models like YOLO and MobileNet work on edge hardware. The right model depends on your accuracy requirements, hardware constraints, and latency budget.

It depends on the model size and task complexity. Consumer GPUs (RTX 3060+) handle most SLMs, NVIDIA Jetson is ideal for embedded systems, and Apple Silicon Macs run CoreML models natively. For smartphones, modern Android devices with 6GB+ RAM can run quantized 1-2B models.

For targeted tasks, yes — often exceeding 95% accuracy. The key is domain-specific fine-tuning: a 1.7B model trained on your vocabulary and use cases outperforms a general-purpose 70B model on those specific tasks. We benchmark accuracy against cloud baselines before deployment.

A typical project takes 6-12 weeks from model selection to production deployment. The first 2-3 weeks focus on model evaluation and optimization for your target hardware. Weeks 4-8 cover integration, fine-tuning on your domain data, and building the inference pipeline. Final weeks handle testing, edge cases, and deployment automation.

On-device AI shifts costs from ongoing OpEx (cloud API calls, bandwidth, per-token pricing) to upfront CapEx (hardware, model optimization). For high-volume use cases — thousands of daily inferences — on-device typically pays for itself within 3-6 months. You also eliminate data egress fees and reduce compliance costs since sensitive data never leaves your infrastructure.