PRODUCT LINE · EFFICIENT AI COMPUTING

CHT-Computing

Bringing dynamic sparse learning to data centers and large-model training and inference, with the aim of making computation more efficient.

Make computation count while preserving model quality

Starting with the target model and task, combine sparse training, model adaptation and runtime evaluation to pursue lower compute and energy requirements within defined quality targets.

From open models to project validation

The public sparse Qwen3-8B model offers a downloadable starting point for evaluation. Project work establishes comparisons for specific models, hardware, precision and workloads, verifying model quality and system benefits separately.