SynFlux: A Unified Multimodal Inference Framework for Edge NPUs

Original Author/Maintainer: tuanhe Source Platform: GitHub Original Link: https://github.com/tuanhe/synflux Publication Date: 2026-06-08

SynFlux is a unified multimodal inference framework specifically designed for edge NPUs. It supports efficient deployment of Large Language Models (LLMs), Vision-Language Models (VLMs), and Vision-Language-Action Models (VLAs). It addresses issues such as limited memory, constrained computing power, and power sensitivity on edge devices, enabling multimodal AI to run on resource-constrained devices and reducing the complexity of edge AI development.

With the rapid development of large language models and multimodal models, deploying AI on edge devices (such as smartphones, IoT terminals, and robot controllers) faces constraints like limited memory, constrained computing power, and power sensitivity. Traditional cloud-based inference has issues like network latency, privacy risks, and offline availability. Therefore, efficiently running multimodal models on edge NPUs is key to AI democratization. SynFlux, as an open-source solution, was born to address this need, aiming to provide a unified framework for complex AI models to run smoothly on the edge.

SynFlux is positioned as a unified multimodal inference framework for edge NPUs, supporting three main model types:

• LLM: Handles pure text input and output, serving as the foundation for intelligent assistants and text generation applications;
• VLM: Understands both images and text simultaneously, enabling functions like image description and visual question answering;
• VLA: Understands vision and language and outputs action commands, which is the core of robot control and embodied intelligence.

The unified framework design allows developers to deploy different models using the same toolchain and API, significantly reducing the complexity of edge AI development.

Tailored to the characteristics of edge NPUs, SynFlux uses multiple optimization techniques to improve inference efficiency:

• Memory Optimization: Reduces model memory usage through quantization, pruning, and KV cache optimization;
• Computation Graph Optimization: Reconstructs and fuses computation graphs to reduce data transfer overhead and improve parallelism;
• Dynamic Batching: Uses an intelligent batching strategy to increase throughput and utilize NPU resources;
• Heterogeneous Scheduling: Coordinates the collaborative work of CPU, GPU, and NPU to select the optimal execution path.

SynFlux has a wide range of application scenarios:

• Smart Terminals: Smartphones/tablets implement localized multimodal AI, such as offline image understanding and intelligent document processing;
• Edge Computing Gateways: Process sensor data and visual inputs in industrial IoT scenarios to reduce cloud latency;
• Robotics and Autonomous Driving: Support VLA models to achieve low-latency perception-decision loops;
• AIoT Devices: Provide local inference for smart homes and wearable devices, protecting privacy and enabling instant responses.

As an open-source project, SynFlux provides tools and reference implementations for the edge AI community:

• Quickly evaluate the performance of different models on target NPUs;
• Learn best practices for quantization and optimization of multimodal models;
• Build prototypes of edge AI applications;
• Participate in contributions to improve support for more NPU hardware and model architectures.

SynFlux represents the trend of AI deployment evolving from cloud-centric to edge-distributed. With the improvement of end-side NPU computing power and advances in model compression technology, running multimodal models with tens of billions of parameters on the edge has become a reality. This trend reduces network dependency, protects privacy, lowers cloud costs, and provides low-latency responses, which is of great significance to AI democratization. Open-source projects like SynFlux accelerate this transformation and provide strong support for fields such as smart terminals, IoT, and robotics.