Baseten Engineering Blog: A Roundup of Model Performance Optimization Practices
The model performance section of the Baseten engineering blog rounds up several inference optimization efforts, including speeding up pyannote's speaker diarization model by 9.6x, speeding up Kimi K3 tokenization by 18x under million-token agent workloads, and performing inference engineering optimization for DeepSeek V4 Pro 0813. The section also covers delta weight synchronization optimization for hosted RL rollouts, Coval voice AI benchmarking, and the practice of building APIs for Kimi K3 and GLM-5.2.
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Blog Inference engineering Model performance How Baseten makes pyannote’s diarization models 9.6x faster Matte Lim 1 other Model performance Optimizing delta weight syncs for managed RL rollouts Paras Stefanopoulos Model performance Baseten leads Coval’s voice AI benchmark Ansel Erol 1 other Model performance The efficient frontier of LLM inference Philip Kiely Model performance Agentic kernels in production Brian Li 2 others Model performance Inference engineering for DeepSeek V4 Pro 0813 Model Performance Team Model performance Making Kimi K3 tokenization 18x faster for million-token agentic workloads Michael Feil Model performance How to build a day-0 API for Kimi K3 Model Performance Team Model performance How we built the new fastest API for GLM-5.2 Alex Korte 6 others 1 2 3 ... 7 requestAnimationFrame(function(){$RT=performance.now()}); Popular models