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JD Retail Technology
JD Retail Technology
May 19, 2025 · Artificial Intelligence

How JD’s Omniforce Boosts Large Model Efficiency with Cloud‑Edge Collaboration

The JD Exploration Institute paper introduces Omniforce, a human‑centered, cloud‑edge collaborative AutoML system that uses model distillation, dynamic data governance, Bayesian‑optimized training, and edge deployment to cut large‑model training costs by 70% and improve inference speed by 30%, powering the JoyBuild platform for broader AI adoption.

AI EfficiencyAutoMLJoyBuild
0 likes · 6 min read
How JD’s Omniforce Boosts Large Model Efficiency with Cloud‑Edge Collaboration
JD Tech
JD Tech
May 15, 2025 · Artificial Intelligence

How JD’s Omniforce Cuts Large‑Model Training Cost by 70% and Boosts Inference Speed 30%

The paper "Omniforce" from JD Exploration Research Institute presents a cloud‑edge collaborative AutoML system that uses model distillation, data governance, Bayesian training optimization, and cloud‑edge cooperation to reduce large‑model training costs by 70% and improve inference efficiency by an average of 30%, offering a reusable technical paradigm for scalable AI deployment.

AI EfficiencyJoyBuildcloud‑edge computing
0 likes · 6 min read
How JD’s Omniforce Cuts Large‑Model Training Cost by 70% and Boosts Inference Speed 30%
Architects' Tech Alliance
Architects' Tech Alliance
Nov 19, 2024 · Industry Insights

Why Computing Power Networks Are the Next Backbone of Digital Infrastructure

The article explains computing power networks as a new information infrastructure that dynamically allocates compute, storage, and network resources across cloud, edge, and end devices, outlines their six key characteristics, measurement units, classifications, essential technologies such as SRv6 and network slicing, and discusses national initiatives and future research directions.

Industry trendsNetwork ArchitectureSRv6
0 likes · 29 min read
Why Computing Power Networks Are the Next Backbone of Digital Infrastructure