Proxis is revolutionizing the AI landscape with its groundbreaking platform dedicated to Large Language Model (LLM) distillation. Designed by industry veterans Liam Collins and Jackson Stokes, Proxis leverages years of expertise in software engineering and machine learning optimization. Liam, a Columbia University and City University of Hong Kong alumnus, left his MBA at Wharton to spearhead this innovative venture. Jackson, with his extensive experience at Google, brings deep insights into optimizing ML models for efficiency. Proxis is not just a product; it's a testament to the founders' commitment to pushing the boundaries of AI technology. Join the waitlist to be among the first to experience the future of AI distillation.

Dedicated Platform for LLM Distillation

Purpose: Proxis is designed to be the first dedicated platform for Large Language Model (LLM) distillation. This means it focuses on taking large, complex AI models and making them more efficient, smaller, and easier to deploy. The main goal is to democratize access to advanced AI technologies by making them more accessible and practical for a wider range of applications and users.

Value: By distilling LLMs, Proxis enables organizations and developers to use powerful AI capabilities without the need for extensive computational resources. This is particularly valuable for startups, small businesses, and developers who may not have the infrastructure to support large-scale AI models. It also opens up possibilities for deploying AI in resource-constrained environments, such as mobile devices or edge computing platforms.

Related Functions: This core function is foundational to all other aspects of Proxis, as it sets the stage for the platform's capabilities in optimizing and deploying AI models.

Optimized ML Model Efficiency

Purpose: One of the key features of Proxis is its ability to optimize machine learning (ML) models to run more efficiently at scale. This involves making models smaller, faster, and more energy-efficient without sacrificing their performance. The focus is on kernel-level and framework-level optimizations, which are critical for deploying AI models in real-world applications.

Value: Optimizing ML models for efficiency is crucial for reducing costs and improving performance in production environments. It allows for faster inference times, lower energy consumption, and the ability to run models on a wider variety of hardware. This is particularly important for applications that require real-time processing or are deployed in environments with limited resources.

Related Functions: This function is closely tied to the distillation process, as optimizing models is a key part of making them more practical and deployable.

Summary

Purpose: Proxis aims to revolutionize the way advanced AI technologies are accessed and utilized by focusing on LLM distillation and ML model optimization. The platform is designed to make cutting-edge AI more accessible, efficient, and practical for a broader range of users and applications.

Value: By addressing the challenges of deploying large-scale AI models, Proxis provides significant value to organizations and developers by reducing costs, improving performance, and expanding the possibilities for AI deployment. This makes advanced AI technologies more viable for a wider range of use cases, from startups to large enterprises.

Related Functions: The Summary feature ties together the core functions of LLM distillation and ML model optimization, highlighting the overall value and uniqueness of Proxis in the AI landscape.