Edge Deployment, Quantization & Optimization
10× faster. Same accuracy. Runs on your hardware.
We take production AI models and make them smaller, faster, and cheaper — without sacrificing accuracy. From INT8/INT4 quantization and knowledge distillation to TensorRT export and full on-device inference pipelines, we make frontier models run within real latency, memory, and cost budgets — on the cloud or at the edge.
What We Deliver
Quantization (INT8 / INT4)
GPTQ, AWQ, GGUF — up to 8× memory reduction with minimal accuracy loss.
TensorRT & ONNX export
Optimised inference for NVIDIA GPUs, CPUs, and mobile hardware.
Knowledge distillation
Compress large teacher models into production-ready student models.
Edge deployment
Jetson Orin, Raspberry Pi, Coral, and OpenVINO-compatible hardware.
Latency & cost profiling
End-to-end benchmarks, bottleneck identification, and optimisation sprints.
Neural architecture search
Task-specific architecture design for constrained hardware budgets.
Use cases by industry
Where teams put Edge & Optimization to work in production.
On-device vision inference on factory-floor edge devices with no cloud round-trip.
Private, on-premises medical inference where data cannot leave the building.
Low-latency perception models within strict embedded compute budgets.
Mobile pose estimation and STT running fully offline on the phone.
Quantized LLM serving that cuts GPU inference cost dramatically.
See it in action
Live demos and sample outputs.
Models, frameworks & tools
Frequently Asked Questions
Ready to start your edge & optimization project?
Let's discuss your requirements and build something production-ready together.
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