Edge AI & LiteRT Deployment

Running a model on the device - a phone, a tablet, a camera, an embedded board - beats the cloud on latency, cost, offline availability, and privacy. In 2026 the way you do that on the TensorFlow stack is LiteRT, Google's production on-device runtime and the successor to TensorFlow Lite.

DeepAdvisors designs, converts, and tunes edge deployments end to end: from a trained Keras or TensorFlow model to a quantized, hardware-accelerated artifact running inside your Android, iOS, or embedded application.

What we do

Model selection and architecture for the edge. Most server-side models are the wrong shape for a phone. We select or adapt architectures that fit your latency and memory budget - mobile-friendly backbones for vision, compact encoders for audio and text, small open-weight language models where on-device generation is justified.

LiteRT conversion and quantization. We convert models to the .tflite flatbuffer format (unchanged by the LiteRT rename) and apply the quantization strategy that your accuracy budget allows: dynamic-range, full-integer with representative datasets, or float16. Every conversion ships with an accuracy report against the original model.

Delegate and accelerator tuning. LiteRT's GPU and NPU acceleration is where most of the performance lives, but it is also where most of the surprises live - unsupported ops falling back to CPU, delegate initialization cost, device fragmentation. We benchmark on your real target devices and tune op selection, delegates, and threading so the fast path is the common path.

Migration from legacy TensorFlow Lite. If you shipped on the org.tensorflow:tensorflow-lite Android artifacts, the TensorFlowLiteSwift pod, or the tflite-runtime Python package, you are on packages that no longer receive feature updates. We migrate applications to the LiteRT packages with no change to your model files. (See our TensorFlow Lite to LiteRT migration tutorial for the details.)

Fleet model updates. A model that ships inside the app binary is a model you can only update with an app release. We design download-and-swap strategies with versioning, signature checks, and staged rollout so you can iterate on the model independently of the app.

Privacy-by-design. For regulated products - health, finance, children's apps - on-device inference means sensitive data never leaves the device. We help you document that architecture for your privacy and security reviews.

Deliverables

• Converted and quantized model artifacts with accuracy and latency reports per target device.
• Integration code in Kotlin, Swift, or C++ with the LiteRT runtime and delegates configured.
• A reproducible conversion pipeline (Python) your ML team can rerun on every retrain.
• Benchmark harness and device matrix results.

Why on-device

• Latency: tens of milliseconds instead of a network round trip.
• Cost: zero per-inference cloud spend at scale.
• Availability: works with no connection.
• Privacy: user data stays on the user's device.

Contact us to discuss your edge deployment. We will ask about your target devices, latency budget, and accuracy requirements, and give you an honest answer on what fits.