Nexa SDK

Product Introduction

1. Nexa SDK is a cross-platform software development kit that enables developers to deploy AI models locally across diverse hardware backends including NPUs, GPUs, and CPUs. It supports text, vision, audio, speech, and image generation models on devices ranging from mobile to edge computing systems. The SDK integrates with Qualcomm Hexagon NPUs, Apple Neural Engine (ANE), Intel NPUs, and popular frameworks like GGUF and MLX.
2. The core value of Nexa SDK lies in its ability to deliver production-ready on-device inference with minimal integration effort, enabling developers to ship AI features faster. It provides first-to-device access to state-of-the-art (SOTA) models like Gemma3n, Llama3.2-NPU variants, and PaddleOCR v4 before competitors.

Main Features

1. Nexa SDK offers unified hardware acceleration across NPUs (Qualcomm, Apple, Intel), GPUs, and CPUs through a single API, eliminating backend-specific optimizations. It automatically selects the optimal backend for each model, such as running parakeet-v3 ASR on Apple ANE or Llama3.2-3B on Qualcomm Hexagon NPUs.
2. The SDK provides pre-optimized SOTA models including multimodal architectures like OmniNeural-4B for text/image/audio understanding and NPU-optimized variants like Phi4-mini-NPU-Turbo. All models undergo quantization via NexaQuant to reduce memory usage by 4X while retaining 99% accuracy.
3. Developers gain access to a model hub with enterprise-ready solutions for real-world applications, such as YOLOv12-N for object detection, SDXL-Base for image generation, and Jan-v1-4B for agentic reasoning. Each model includes platform-specific builds for Android, iOS, Windows, macOS, and Linux.

Problems Solved

1. Nexa SDK eliminates the complexity of deploying AI models across fragmented hardware ecosystems, solving compatibility issues between NPU architectures (Qualcomm vs. Apple vs. Intel) and framework formats (GGUF, MLX, ONNX). It abstracts low-level hardware differences through a unified inference engine.
2. The product targets developers building on-device AI applications requiring low latency and privacy compliance, including mobile app engineers, edge computing specialists, and embedded systems developers. Enterprise teams deploying OCR (PaddleOCR v4), ASR (parakeet-v3), or multimodal agents (OmniNeural-4B) are primary users.
3. Typical use cases include real-time speech-to-text transcription on iPhones using Apple ANE, energy-efficient image generation via SDXL-Base on Qualcomm NPUs, and deploying Llama3.2-3B-NPU-Turbo for chatbots on Android devices without cloud dependencies.

Unique Advantages

1. Unlike runtime frameworks that require manual backend configuration, Nexa SDK automatically deploys models to the most capable available accelerator (NPU > GPU > CPU) with zero code changes. It outperforms alternatives by achieving >5x faster NPU inference speeds through kernel-level optimizations for Qualcomm Hexagon and Apple ANE.
2. The SDK introduces NexaQuant, a proprietary compression technology that reduces model sizes by 4X while maintaining 99% of original accuracy through mixed-precision quantization. This enables deployment of 8B-parameter models like Llama-3.1-8B on devices with ≤4GB RAM.
3. Competitive differentiation comes from exclusive early access to cutting-edge models—such as Gemma3n-E4B and Qwen3-4B variants—before public release. Partnerships with model developers ensure Nexa SDK users deploy SOTA architectures 6-12 months ahead of open-source alternatives.

Frequently Asked Questions (FAQ)

1. What hardware platforms does Nexa SDK support? The SDK supports Qualcomm Snapdragon NPUs (8th Gen and newer), Apple Silicon M-series/MacBook ANE, Intel Core Ultra NPUs, NVIDIA GPUs (CUDA 12+), and x86/ARM CPUs. Android, iOS, Windows, macOS, and Linux are fully supported.
2. How does Nexa SDK optimize models for NPUs? Models undergo hardware-aware pruning, kernel fusion for NPU instruction sets (Hexagon Tensor Accelerator, Apple ANE Matrix Coprocessors), and memory alignment optimizations. This reduces latency by 62% compared to ONNX Runtime for equivalent NPU targets.
3. What are the benefits of NexaQuant compression? NexaQuant employs dynamic range quantization with layer-wise error correction, enabling 4-bit inference without accuracy drops. This reduces Phi4-mini from 1.6GB to 400MB while maintaining 99.3% of its FP16 accuracy on language tasks.
4. Can I deploy custom models alongside pre-optimized ones? Yes, Nexa SDK accepts PyTorch, TensorFlow, and ONNX models, which are automatically converted to NPU/GPU-optimized formats via the CLI. Custom quantization profiles can be applied using the nexaquant --mode=custom flag.
5. Are MLX-format models compatible with Android devices? Nexa SDK converts MLX models to GGUF format with NPU-specific optimizations, enabling Apple ANE-optimized models like Llama3.2-3B-Intel-NPU to run on Qualcomm devices through automatic cross-compilation during deployment.