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Diffusion vs. Autoregressive Language Model Performance

Diffusion vs. Autoregressive Language Model Performance

1
posts
2026

This post characterizes the performance of Diffusion Language Models (DLMs) against Autoregressive Language Models (ARMs). It analyzes trade-offs in arithmetic intensity, context scaling, batched inference, and sampling step reduction. The work highlights that while DLMs offer parallelism, they struggle with long contexts, and ARMs benefit more from batching. Opportunities for DLM acceleration are identified, particularly in reducing sampling steps for competitive latency.

2026

Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models

8/7/2026

This post presents a comprehensive study of the performance characteristics of ARMs and DLMs, combining theoretical analysis with empirical profiling. It demonstrates that DLMs can achieve higher arithmetic intensity but fail to scale effectively with longer contexts. The work explores block-wise decoding for DLMs to improve context scaling and examines batched inference, finding ARMs exhibit superior throughput. It also identifies reducing sampling steps as key for DLM inference acceleration.