China Mobile Cloud 与 Zhipu 达成合作，推进 GLM 集成与 AI 编程产品

The framework shifts efficiency analysis from raw benchmark scores to value-per-joule, a metric that will matter as data-centre energy costs and grid constraints increasingly cap AI deployment. Teams choosing models or hardware can use it to justify smaller models like Gemma 4 31B over larger ones when value per joule outweighs per-task output. It pairs with GDPWorks for organisations benchmarking agentic work beyond the original 44 occupations.

Stanford AI Lab@StanfordAILab11 sourcesSource

The technique shows that partitioning a single GPU with CUDA Green Contexts removes the resource contention between dispatch warps and GEMM warps that limits fused MoE operators like Mega MoE. Practitioners serving DeepSeek-V4.1-Flash or GLM-5.2 on Hopper or Blackwell clusters can adopt the open-source operator to cut decode latency without extra hardware. The results on H20 GPUs, a China-market chip, make it especially relevant for deployments where H100-class hardware is unavailable.