Deploy capable AI models on constrained hardware — factory floors, vehicles, medical devices, retail kiosks. We select, optimise, and deploy SLMs that actually work in production on edge hardware.
Factory floors, vehicles, and remote sites have unreliable or no internet connectivity
Cloud AI latency (200–2000ms round-trip) is too slow for real-time physical control loops
Data sovereignty rules out cloud transmission for sensitive sensor data in regulated industries
Most teams don't know which small models actually work in production vs. only in benchmarks
Quantization and runtime selection for edge hardware is highly specialised — standard guides don't cover it
Six stages from hardware constraint mapping to production edge deployment with OTA updates.
Document hardware specs (RAM, CPU/GPU/NPU, power budget), connectivity profile, latency requirements, and operating environment (temperature, vibration, dust).
Benchmark Phi-4-mini, Gemma 3 1B/4B, SmolLM2, and Qwen 2.5 small models against your task on your target hardware — not just cloud benchmarks.
Convert to INT4 GGUF (llama.cpp), INT8 ONNX, or TFLite based on the target runtime and hardware accelerator (NVIDIA Jetson, Snapdragon, Apple Neural Engine).
Choose between llama.cpp (CPU/GPU), ONNX Runtime (cross-platform), ExecuTorch (mobile/embedded), or Transformers.js (browser/WASM) based on your platform.
Build the REST API, embedded C++ bindings, or WebAssembly module that integrates with your existing edge application.
Implement model versioning and push-on-reconnect updates so edge devices receive new model versions without manual intervention.
You build products for factory floors, vehicles, medical devices, or IoT platforms where cloud connectivity is unavailable, too slow, or prohibited. You want AI that runs fully offline on constrained hardware. You're an automotive OEM, industrial manufacturer, medical device company, or IoT platform builder.
A Raspberry Pi 5 (8GB RAM) can run SmolLM2 1.7B INT4 at ~3 tokens/second via llama.cpp — sufficient for keyword extraction, classification, and simple Q&A. For real-time responses, a Jetson Orin NX (16GB, 1024-core GPU) runs Phi-4-mini 3.8B INT4 at 20–40 tokens/second.
Phi-4-mini 3.8B leads on reasoning tasks (math, structured analysis). Gemma 3 4B leads on multilingual and general instruction following. SmolLM2 1.7B is fastest on CPU-only hardware. Qwen 2.5 1.5B is strongest for Chinese/multilingual. We benchmark all candidates on your specific task.
For structured tasks (classification, extraction, templated generation), SLMs achieve 80–95% of GPT-4 accuracy after task-specific fine-tuning. For open-ended reasoning, expect 60–80%. We always run a benchmark on your specific task before committing to a deployment.
Yes. We implement an OTA update pipeline that pushes new quantized model files to edge devices when they reconnect. Model versioning, rollback support, and staged rollout (canary → 10% → 50% → 100%) are all included.
Yes. We've designed AI pipelines for automotive applications using ONNX Runtime with automotive-grade Qualcomm Snapdragon or NVIDIA DRIVE hardware. OBD-II integration, CAN bus data ingestion, and AUTOSAR-compatible integration patterns are all in scope.
Let's discuss how this service can address your specific challenges and drive real results.