Cross-Device End-Side Generative AI Multi-Modal Benchmarking with Nexa Compressed Inference

Executive Summary

Nexa Native Inference Framework makes the deployment of generative AI models on the device side seamless and efficient. The technology supports a wide range of chipsets including AMD, Qualcomm, Intel, NVIDIA, and homegrown chips, and is compatible with all major operating systems. We provide benchmark data for generative AI models on a variety of common tasks, each tested at TOPS performance level on different types of devices.

Core strengths:

1. multimodal capability - be in favor ofText, audio, video and visualGenerative AI-like tasks
2. Wide range of hardware compatibility - Runs AI models on PCs, laptops, mobile devices, and embedded systems
3. leading performance - With our edge inference framework, NexaQuant, models run 2.5x faster and storage and memory requirements are reduced by 4x while maintaining high accuracy

Why end-side AI?

Deploying AI models directly on the device side has several advantages over relying on cloud APIs:

• Privacy and Security - Data retention on the device side ensures confidentiality
• reduce costs - No need to pay for expensive cloud-based reasoning
• Speed and Response - Low-latency inference without relying on the network
• offline capability - AI applications can still be used in low connectivity areas

With Nexa edge inference technology, developers can efficiently run generative AI models on a wide range of devices while minimizing resource consumption.

New Trends in Multimodal AI Applications

Nexa AI End-side deployment supportMultimodal AI, enabling applications to handle and integrate multiple data types:

• Text AI - Chatbots, document summarization, programming assistants
• Speech to Speech AI - Real-time voice translation, AI voice assistant
• Visual AI - Target detection, image description, document OCR processing

This is accomplished through the use ofNexaQuantOur multimodal models achieve excellent compression and acceleration while maintaining top performance.

Cross-Device Generative AI Task Performance Benchmarks

We provide benchmarking data for generative AI models on a variety of common tasks, each tested at the TOPS performance level on different types of devices. If you have a specific device and target use case, you can refer to similarly performing devices to estimate processing power:

Generative AI tasks covered:

• Voice to Voice
• Text to Text
• Visual to text

Covered device types:

• Modern Notebook Chips - Optimized for desktop and laptop native AI processing
• flagship mobile chip - AI models running on smartphones and tablets
• embedded system (~4 TOPS) - Low Power Devices for Edge Computing Applications

Speech-to-speech benchmarking

Evaluating Real-Time Speech Interaction Capabilities with Language Models - ProcessingAudio input generates audio output

Speech-to-Speech Benchmarking Using Moshi with NexaQuant

Text-to-text benchmarking

valuationGenerate text based on text inputAI model performance

Text-to-text benchmarking using llama-3.2 with NexaQuant

Visual-to-text benchmarking

Evaluating AI Analyzing Visual InputsThe ability to generate responses, extract key visual information, and dynamic guidance tools -Visual Input, Text Output

Visual-to-Text Benchmarking Using OmniVLM with NexaQuant