Model Quantization & Pruning
Compress trained models to fit on-device memory and compute budgets with measured accuracy tradeoffs.
Edge AI Deployment
Edge AI deployment runs trained models directly on-device, on phones, embedded boards, or IoT hardware, instead of a cloud server, trading some model size and accuracy for offline operation, lower latency, and no per-inference cloud cost. The engineering problem is different from cloud-hosted AI work: it's quantization, format conversion, and fitting a model's memory and compute footprint into hardware measured in megabytes and milliwatts, not GPU-hours.
2-3 Weeks
Typical Delivery Timeline
Getting a Model to Actually Run On-Device
1
Model Compression
- Quantization (FP32 to FP16/INT8) to cut model size and speed up inference at a measured accuracy cost
- Pruning to remove redundant weights and channels
- Knowledge distillation into a smaller student model
- Choosing an edge-native architecture (MobileNet, EfficientNet-Lite) from the start rather than compressing a large model after the fact
2
Format Conversion & Runtimes
- Converting trained models to TensorFlow Lite, Core ML, or ONNX Runtime depending on the target platform
- Operator compatibility checks, since not every training-framework op has an edge-runtime equivalent
- Hardware-specific acceleration: NNAPI on Android, the Neural Engine via Core ML on iOS, TensorRT on NVIDIA Jetson
- Validating numerical parity between the original and converted model before shipping
3
Offline Inference & Sync
- Fully offline operation with no network dependency for the inference path itself
- On-device caching and local storage for model weights and inputs
- Delta updates for shipping new model versions without re-downloading the full model
- Sync strategies for uploading results or telemetry once connectivity returns
4
Latency & Battery Tradeoffs
- Measuring inference time and power draw on the actual target device, not a dev laptop
- Batching and duty-cycling to control thermal and battery impact on mobile
- Input resolution and model size chosen against a concrete latency and battery budget
- Managing cold-start vs. keep-warm tradeoffs on mobile OSes that suspend background processes
5
Deployment & Update Pipeline
- Packaging the model into the app binary or mobile CI/CD pipeline
- Over-the-air model updates without a full app store release cycle
- Versioning and rollback if a new model regresses in the field
- Fleet monitoring tying crash/performance telemetry back to a specific model version
Our Edge AI Deployment Services
Getting trained models to actually run on constrained hardware
Mobile Deployment
Core ML for iOS and TensorFlow Lite for Android, integrated with hardware acceleration where available.
Embedded & IoT Deployment
ONNX Runtime and platform-specific runtimes for Jetson, Coral, and other embedded boards.
Offline Inference Pipelines
Fully on-device inference paths with no network dependency, plus sync logic for when connectivity returns.
Latency & Power Optimization
Tuning model size, batching, and duty cycling against real device latency and battery budgets.
OTA Model Update Pipelines
Ship new model versions to a device fleet without a full app store release cycle.
Why Teams Pick SpeedMVPs for Edge AI
Benchmarked on target hardware, not a dev machine
A model that runs fast on a laptop can be unusably slow on the actual phone or embedded board it's shipping to. We measure on the real target device before calling anything done.
Compression tradeoffs are quantified, not assumed
Quantization and pruning cost some accuracy. We measure exactly how much, on your validation set, so you're making an informed tradeoff instead of guessing.
Update pipeline built in from day one
Models drift and improve. We ship a way to update the on-device model without forcing a full app store release for every iteration.
Offline-first architecture
When the requirement is 'must work with no signal,' that constrains the whole pipeline, not just the model. We design for it from the start.
Why Teams Pick SpeedMVPs for Edge AI
Benchmarked on target hardware, not a dev machine
A model that runs fast on a laptop can be unusably slow on the actual phone or embedded board it's shipping to. We measure on the real target device before calling anything done.
Compression tradeoffs are quantified, not assumed
Quantization and pruning cost some accuracy. We measure exactly how much, on your validation set, so you're making an informed tradeoff instead of guessing.
Update pipeline built in from day one
Models drift and improve. We ship a way to update the on-device model without forcing a full app store release for every iteration.
Offline-first architecture
When the requirement is 'must work with no signal,' that constrains the whole pipeline, not just the model. We design for it from the start.
Edge AI Deployment, FAQ
It depends on the model and task, but INT8 quantization typically costs somewhere between negligible and a few points of accuracy for well-behaved architectures, more for models that weren't designed with quantization in mind. We always measure this on your validation set before deployment rather than quoting a generic number, because the actual cost varies a lot by architecture.
It depends on your target platform: Core ML is Apple's native format and gets you access to the Neural Engine on iOS/macOS devices, TensorFlow Lite is the standard for Android and general embedded use, and ONNX Runtime is the more portable option when you need to support multiple platforms or hardware vendors from one export. Cross-platform apps sometimes need two export paths from the same trained model.
Yes, that's the core use case for edge deployment: once the model and its weights are on the device, inference doesn't need a network call. What still typically needs connectivity is shipping model updates and, if your app does it, syncing logged results or telemetry back to a server, both of which we design to queue locally and sync opportunistically.
It removes per-inference cloud compute cost, which matters at volume, but it isn't free: you pay in engineering time for compression and platform-specific optimization, and in a smaller or less accurate model than you could run unconstrained on a GPU server. It makes sense when latency, offline operation, or per-inference cost at scale justify that tradeoff; for low-volume or accuracy-critical workloads, cloud inference is often still the simpler answer.
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Portfolio: AI Products Built for Global Startups
From content platforms and AI assistants to analytics dashboards and fintech solutions: see how we've transformed ideas into production-ready MVPs in 2-3 weeks across diverse industries. Each product launched successfully, serving users globally.
UseArticle
AI-powered content creation and management platform that helps teams produce high-quality articles at scale.
AgentHi
Intelligent virtual assistant that streamlines customer support and automates routine business tasks.
StatsHub
Comprehensive analytics dashboard providing real-time insights and data visualization for businesses.
Harimaxx
Personal fitness companion with AI-driven workout plans and nutrition tracking for optimal health.
Vaga
Smart travel planning app that curates personalized itineraries and local experiences.
FoodScan
Nutrition analysis app that scans food items and provides detailed nutritional information instantly.
MyJobReach
Job matching platform connecting talented professionals with their dream opportunities.
TravelGram
Social platform for travelers to share experiences, discover destinations, and connect globally.
SuperStatz
Advanced sports statistics platform delivering in-depth analysis and performance metrics.
Cashbook
Simple expense tracking and budgeting app that helps users manage their finances effortlessly.
TypeFast
Typing speed improvement platform with gamified lessons and real-time performance tracking.
Easy Loan
Streamlined loan management system that simplifies borrowing and lending processes.
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