Is it possible to run large language models on edge devices?

Small quantized language models can run on capable edge hardware for jobs like summarisation or voice commands. Very large models still require data-centre GPUs.

Is edge AI safer than cloud AI?

It can lower exposure since raw data stays on site, but the devices themselves need securing and the models need protection from tampering.

What hardware do edge AI deployments use?

Common choices are NVIDIA Jetson modules, industrial PCs fitted with GPUs or NPUs, Intel CPUs running OpenVINO, and ARM devices with dedicated AI accelerators.

How much accuracy do compressed models lose?

Properly quantized models generally lose little accuracy, though the drop differs by model and task, so always validate on the target device using real data.

On-Device AI Guide: Deploying Models at the Edge

Edge AI explained: when to run models on devices rather than in the cloud, how quantization compresses them, and which tools deploy them.

Edge AI executes models right on devices — cameras, gateways, kiosks, industrial PCs — rather than shipping data to the cloud. It is the correct choice when you need low latency, offline operation, data privacy or reduced bandwidth costs. Quantization, pruning and distillation make models fit, and runtimes like ONNX Runtime, TensorRT, OpenVINO or llama.cpp deploy them.

Edge AI defined

With edge AI, inference takes place where the data originates. A production-line camera inspects parts on its own; a warehouse gateway reads labels without a round trip to a data centre; a kiosk understands speech even if the network goes down.

Training generally still happens in a central location. The edge device gets a compact, optimised model and runs it locally.

Edge or cloud: when to run AI on the device

Deploying at the edge justifies the additional engineering when one or more of these apply:

Shrinking models to fit edge hardware

Quantization saves weights as 8-bit or 4-bit integers rather than 16- or 32-bit floats, reducing memory and accelerating inference on supported hardware. Pruning strips out weights that add little. Distillation trains a small student model to mimic a larger teacher.

For language workloads, small open models of roughly one to eight billion parameters can run on capable edge hardware once quantized. For vision, compact detection and segmentation models are well proven on embedded GPUs and NPUs.

Edge AI tools and runtimes

Popular options include ONNX Runtime for portability across hardware, NVIDIA TensorRT for Jetson and other NVIDIA devices, Intel OpenVINO for Intel CPUs and accelerators, and llama.cpp for running quantized language models on CPUs and modest GPUs.

Choose the runtime that suits the target hardware, then benchmark speed and accuracy on the real device — numbers from a developer laptop seldom carry over directly.

Safely deploying and updating edge models

Device fleets call for the same rigour as any production system:

Should you combine edge and cloud?

Frequently, yes. The device takes care of quick, routine decisions and filters data; the cloud takes on heavy analysis, retraining and fleet management. Latency stays low and privacy stays high while you still gain from central learning.

On-device and edge AI is part of the AI infrastructure work we deliver — our AI services page describes typical deployments.

Latency: decisions are needed within milliseconds, for example rejecting a defective part.
Connectivity: sites suffer from poor or intermittent networks.
Privacy: raw video, audio or personal data must stay on the premises.
Bandwidth cost: sending high-resolution video to the cloud is costly.
Resilience: the process has to keep running through outages.
Version each model and roll out step by step, starting with a subset of devices.
Maintain a rollback path in case accuracy or performance falls.
Gather lightweight telemetry — latency, confidence, error rates — without transmitting raw data.
Sample difficult cases from time to time (with consent) to improve the next model.
Protect devices and model files from tampering.