Inference Set to Consume 70% of AI Compute Power, Leaving 30% for Training

Zhang Lu, a Silicon Valley investor, argues that AI's focus is shifting from training to inference—now accounting for half of current compute and projected to reach 70%—while communication energy, data quality, physical AI, and edge deployment become the next critical bottlenecks and opportunities across medical, space, and nano‑robotics applications.

AI applicationsAI inferencePhysical AI

0 likes · 19 min read

Inference Set to Consume 70% of AI Compute Power, Leaving 30% for Training

Radish, Keep Going!

Feb 4, 2025 · Artificial Intelligence

How DeepSeek Is Redefining AI: Efficiency, Open‑Source Impact, and Future Trends

The article reviews DeepSeek's breakthrough in inference efficiency, explores the trade‑offs of model distillation, compares open‑source and closed‑source ecosystems, examines shifting compute demands, highlights Chinese engineering innovations, and outlines future directions for AI development.

AI inferenceDeepSeekcompute optimization

0 likes · 9 min read

How DeepSeek Is Redefining AI: Efficiency, Open‑Source Impact, and Future Trends

JD Tech

May 17, 2024 · Artificial Intelligence

Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency

The article details how JD's advertising retrieval platform tackles the core challenge of balancing limited compute resources with massive data by optimizing compute allocation, improving model scoring efficiency, and enhancing iteration speed through distributed execution graphs, adaptive algorithms, and platform‑level infrastructure improvements.

ANNAdvertisingcompute optimization

0 likes · 24 min read

Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency

JD Retail Technology

Apr 24, 2024 · Backend Development

Design and Optimization of JD Advertising Retrieval Platform: Adaptive Compute Allocation, High‑Efficiency Search Engine, and Platform‑Scale Infrastructure

The article presents a comprehensive overview of JD's advertising retrieval platform, detailing how it balances limited compute resources with massive data through adaptive compute allocation, distributed execution graphs, elastic systems, and multi‑stage algorithmic improvements to achieve high‑performance, scalable ad matching.

AdvertisingJD.comMachine Learning

0 likes · 22 min read

Design and Optimization of JD Advertising Retrieval Platform: Adaptive Compute Allocation, High‑Efficiency Search Engine, and Platform‑Scale Infrastructure

DataFunTalk

Nov 21, 2023 · Artificial Intelligence

Improving Efficiency of Large-Scale Distributed Training for Large Language Models

Recent advances in large language models have dramatically increased model size and training data, leading to soaring computational costs; this article examines the scaling trends, hardware utilization challenges, distributed training techniques, and ethical considerations, highlighting methods to improve efficiency, reduce costs, and mitigate environmental impact.

AI ethicsEfficiencycompute optimization

0 likes · 29 min read

Improving Efficiency of Large-Scale Distributed Training for Large Language Models