Search Results for "parallel computing"
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Showing 2 open source projects for "parallel computing"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Build a Custom Ad Server in Just WeeksAdzerk's APIs make it easy for engineers and PMs to build their own server-side, fully-customized ad server. Top e-retailers and user communities use Adzerk to build innovative ad servers to promote anything from native ads to internal content to sponsored listings (where vendors and sellers pay for their organic listing to be promoted in search and browsing results). Engineers reliably see a 90%+ reduction in dev time using Adzerk’s APIs versus doing it entirely from scratch. Adzerk’s customer list includes Fortune 500 brands, public companies, and unicorn startups, including Bed Bath & Beyond, LiveNation/TicketMaster, Wattpad, TradingView, imgur, Strava, and many more. Our Ad.Product community makes it easy for product managers, engineers, ad ops, and others to discover and discuss how to build innovative, user-first ad platforms.
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Parallax
Parallax is a distributed model serving framework
Parallax is a decentralized inference framework designed to run large language models across distributed computing resources. Instead of relying on centralized GPU clusters in data centers, the system allows multiple heterogeneous machines to collaborate in serving AI inference workloads. Parallax divides model layers across different nodes and dynamically coordinates them to form a complete inference pipeline. A two-stage scheduling architecture determines how model layers are allocated to... -
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Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
Xtuner is a large-scale training engine designed for efficient training and fine-tuning of modern large language models, particularly mixture-of-experts architectures. The framework focuses on enabling scalable training for extremely large models while maintaining efficiency across distributed computing environments. Unlike traditional 3D parallel training strategies, XTuner introduces optimized parallelism techniques that simplify scaling and reduce system complexity when training massive models. The engine supports training models with hundreds of billions of parameters and enables long-context training with sequence lengths reaching tens of thousands of tokens. ...
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