Researchers develop the first quantitative theory to predict neural scaling law exponents for large language models based on two key statistical properties of natural language: token correlation decay and conditional entropy decay. The theory matches experimental results from GPT-2 and LLaMA models trained on TinyStories and WikiText without requiring free parameters or synthetic data.
Compute:Arena is a community-driven platform for benchmarking local AI models across different hardware and software configurations. Users submit performance metrics for various models including Qwen, Llama, and Gemma variants running on Apple Silicon and AMD GPUs, with measurements of throughput and prompt processing speed.