Published by: Ling Team, AI@Ant Group
Search for original: Link
Key Take Aways
The development of Ling’s series of large language models demonstrates that state-of-the-art 300B MoE models can be effectively trained on lower-performance hardware, reducing costs significantly without sacrificing performance.
Utilising heterogeneous computing infrastructure and optimisation techniques, the models achieved approximately 20% cost savings during training, making large-scale AI deployment more accessible for organisations with constrained budgets.
The models, Ling-Lite (16.8B parameters) and Ling-Plus (290B parameters), exhibit performance comparable to industry benchmarks, highlighting scalable solutions suited for resource-limited environments.
Innovative engineering strategies—including model architecture refinement, training anomaly handling, and efficient data and evaluation pipelines—are critical for stabilising training and improving model robustness.
Emphasis on optim...
Access this content for FREE by signing up for ROAR Membership.
Join with a Basic (free) or Plus membership (for extra features).