Topic: quantization

This RICAM workshop examines the numerical foundations of reliable deep-learning computation. Low-precision and mixed-precision arithmetic, quantization and parallel GPU computation enable larger models and deployment on constrained devices, while introducing questions about error, stability and performance. Researchers from theoretical and applied communities will compare methods for stable training and efficient inference and identify directions for further work. The programme brings together expertise in numerical analysis, machine learning and hardware-aware computation. The in-person wor

Fireworks AI co-founder Benny Chen explains the infrastructure required to serve machine-learning models at scale, from GPU procurement and kernels to runtimes and request routing. He discusses power constraints, hardware utilization, mixture-of-experts models, quantization, reinforcement-learning efficiency and speculative decoding. The conversation also covers distributed training across data centres, sparse autoencoders, the constraints on open-source interpretability research, and the relationship between open models and compute availability. Hosted by Ravid Shwartz Ziv and Allen Roush; th