NexaSDK for Mobile
AIA cross-platform SDK to run and ship LLMs, multimodal, ASR and TTS models on mobile, PC, automotive and IoT with NPU/GPU/CPU acceleration.
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Free Available
Starting from Free
Premium plans available
NexaSDK for Mobile
AI
A cross-platform SDK to run and ship LLMs, multimodal, ASR and TTS models on mobile, PC, automotive and IoT with NPU/GPU/CPU acceleration.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About NexaSDK for Mobile
NexaSDK for Mobile is a developer toolkit that enables running and deploying large language models, multimodal models, automatic speech recognition (ASR), and text-to-speech (TTS) directly on-device across iOS and Android (and broader device classes). It provides runtimes and tooling powered by the NexaML engine to run workloads on NPUs (including Apple Neural Engine), GPUs and CPUs, delivering low-latency, private, and production-ready inference. The SDK includes platform-specific bindings (Android, CLI/Python integrations and Linux support), model conversion and optimization utilities, and runtime acceleration to minimize resource use and latency while maintaining on-device privacy and reduced cloud costs.
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Key Features
Cross-Platform Runtimes: Provides unified runtimes and SDK bindings for Android, Linux, CLI and Python to build and run models on mobile, PC, automotive, and IoT platforms.
Hardware Acceleration Support: Optimized execution across NPUs, GPUs and CPUs (including Apple Neural Engine support) to deliver low-latency inference and efficient power usage on-device.
Model Compatibility and Conversion: Tools to import, convert, and optimize LLMs and multimodal models for on-device execution, including quantization and engine-specific optimizations to reduce memory and compute footprint.
Multimodal & Speech Support: First-class support for LLMs, multimodal models, ASR and TTS pipelines so apps can run voice, text and vision capabilities locally without cloud dependency.
NexaML Engine: Proprietary runtime engine that orchestrates model execution, memory management, and operator kernels to maximize throughput and stability across diverse hardware.
Privacy-First Local Inference: Enables fully on-device model inference to keep sensitive data local, reducing latency and removing need for continuous cloud connectivity.
Developer Tooling & Samples: Includes SDK integrations, sample applications and documentation to accelerate prototyping and production deployment on mobile devices.
Profiling and Performance Tuning: Tools for benchmarking, profiling, and tuning model performance on target devices to balance latency, accuracy and power consumption.
Deploy LLMs and multimodal models on-device (iOS & Android)
Support for ASR and TTS pipelines
Runtimes optimized for NPU, GPU and CPU
SDKs for Android, iOS, Linux, Python and CLI
Local inference for privacy and low-latency
Production tooling for automotive and IoT integration
Run LLMs, multimodal, ASR and TTS models locally on device
Support for NPUs, GPUs and CPUs (hardware-accelerated inference)
SDK tooling for CLI, Python, Android and Linux
Powered by NexaML inference engine
On-device/private inference for data privacy and low latency
Production-ready deployment workflows for mobile, PC, automotive and IoT
Support for platform-specific accelerators (e.g., Apple Neural Engine)
Cross-platform model packaging and shipping to devices
Use Cases
Offline Mobile Assistant: Embedding an LLM and TTS on iOS/Android to provide conversational assistant capabilities without sending user data to the cloud, improving privacy and latency.
On-Device Speech Interfaces for Automotive: Running ASR and TTS locally in automotive head units to enable responsive voice control and navigation while preserving privacy.
Multimodal AR/VR Experiences: Deploying vision+language models on-device for real-time scene understanding and interactive augmented reality without a network round-trip.
Edge IoT Inference: Running lightweight multimodal or classification models on IoT devices to process sensor data locally and reduce cloud costs and bandwidth.
Desktop Productivity Apps: Shipping LLM-powered writing, search, or summarization features in desktop applications with low latency and offline capability.
Cost-Reduction for High-Volume Inference: Moving inference from cloud to device to lower recurring cloud compute costs and reduce server-side infrastructure requirements.
Integrate on-device LLMs into mobile apps
Build multimodal AR/assistant experiences with local inference
Deploy speech recognition and TTS in offline/edge scenarios
Embed AI into automotive infotainment and ADAS
Run private inference on IoT and embedded devices
Deploy conversational LLMs entirely on-device for mobile apps to preserve user privacy and reduce latency
Integrate multimodal perception (vision + language) into automotive infotainment or driver assistance systems
Embed on-device ASR and TTS for offline voice assistants on mobile and IoT devices
Ship optimized models across heterogeneous hardware (NPU/GPU/CPU) in production fleets
Prototype and test local inference workflows using CLI or Python before mobile integration
