新闻
Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models
Apple Machine Learning Research · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Apple researchers compared diffusion language models to autoregressive models, finding diffusion models achieve higher arithmetic intensity but struggle with longer contexts.
- 为何重要
- Engineers optimizing inference performance need to understand when parallel decoding helps versus when sequential generation remains superior for their workloads.
- 注意
- Diffusion models require reducing sampling steps to match autoregressive latency, and autoregressive models still outperform on batched inference throughput.
收听本摘要
- llm
- language model
- token
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