Fastagger provides an edge AI runtime platform that enables developers to deploy and run machine learning models directly on edge devices. This SDK facilitates secure, local data processing and supports offline operation for embodied AI agents across various hardware. The platform optimizes model performance for resource-constrained environments, enabling intelligent decision-making where connectivity is limited.
Funding
Funding not disclosed
GFFounders
Product
Problem
Traditional cloud-based machine learning systems require constant internet connectivity, raise data privacy concerns, and struggle to run multimodal large language models (LLMs) efficiently. These systems also face limitations in processing power and cross-platform compatibility, hindering their deployment on edge devices.
Solution
Fastagger provides software infrastructure that enables machine learning and AI models to run directly on edge devices, addressing the limitations of cloud-based systems. The platform utilizes techniques like multiparty computation (MPC), fully homomorphic encryption (FHE), and trusted execution environments (TEE) to ensure secure local processing. By optimizing models for edge deployment, Fastagger allows for offline operation, cross-platform compatibility, and the ability to run multimodal LLMs without relying on constant internet access or cloud infrastructure.
Target Audience
The primary target audience includes organizations and developers seeking to deploy machine learning models on edge devices while maintaining data privacy and minimizing reliance on cloud infrastructure.
Features
- Proprietary compression algorithms to optimize model size and performance on edge devices.
- Secure local processing using multiparty computation (MPC), fully homomorphic encryption (FHE), and trusted execution environments (TEE).
- Offline and limited connectivity support, enabling ML models to function without constant internet access.
- Support for multimodal LLMs, extending the capabilities of edge devices beyond traditional cloud-based systems.
- High-performance edge computing, leveraging the processing power of edge devices for high-speed inference.
- Cross-platform compatibility, allowing ML models to run seamlessly on different edge devices.