* Motivation * System Design * Performance Evaluation * Introducing a metric for LLM inference on Low-end GPU * Performance on RTX 4090 v.s. vLLM on A100 * Performance of Multi-Services on Single A100 * Performance on L4 and T4 * FEDML Nexus AI Serverless Model Endpoint: Serving LLM on decentralized spot GPU instance * Unlock AI x
On November 7, 2023 FEDML team held a live webinar introducing FEDML Nexus AI, and some of its key capabilities. The webinar also included a hands-on demo on (1) how to use Launch to tap into a massive GPU marketplace (multi-cloud and multi-provider) to auto-provision and effortlessly run an AI
💡We have a webinar on Tuesday Nov 7 at 11am PT/2pm ET at which we’ll introduce our FEDML Nexus AI platform and show a live-demonstration of Studio: Register for the webinar here Table of contents: Introduction FEDML Nexus AI Overview LLM use cases The challenge with LLMs Why
SUNNYVALE, CA – October 24, 2023 – Today, FEDML, a rapidly growing startup in artificial intelligence (AI), officially announced the release of FEDML Nexus AI, offering the next generation of cloud services and platform for generative AI. As large language models (LLMs) and other generative AI applications gain prominence and global GPU
Any video content platform experience can now be vastly improved with AI-based video recommendation that suggests the most optimal and personalized videos for each user. Community powered AI “Superclusters” The use of AI in enterprise products and services is booming (Forbes on how businesses are using AI in 2023 article)
FedML AI platform is democratizing large language models (LLMs) by enabling enterprises to train their own models on proprietary data. Today, we release FedLLM, an MLOps-supported training pipeline that allows for building domain-specific LLMs on proprietary data. The platform enables data collaboration, computation collaboration, and model collaboration, and supporting training
Detailed instructions of a simple ML training demo for a first time FedML user. Table of Contents Overview Federated Machine Learning Overview This Demo: An Introductory “Hello World” Example for FedML Octopus MLOps FedML's Workflow and Objects Prerequisites Steps: 1. Install FedML 2. Download our FedML Repository from GitHub 3.