two-tower

NVIDIA introduces TwoTower, a method that decouples context representation and denoising in diffusion language models, achieving 2.42x throughput while retaining 98.7% of autoregressive quality on a 30B MoE backbone.

A 4-bit quantization of NVIDIA's TwoTower block-diffusion LM (Nemotron-Labs-TwoTower-30B-A3B-Base) that compresses both towers into ~38 GB, enabling single-GPU inference at slow speeds (~2-4 tok/s).

The paper proposes Nemotron-TwoTower, a diffusion language model that decouples context representation and denoising using a frozen autoregressive tower and a trainable diffusion denoiser, achieving 98.7% of baseline quality with 2.42x throughput.

NVIDIA released Nemotron-TwoTower-30B-A3B-Base-BF16, a diffusion-based language model that uses block-wise autoregressive diffusion to generate text by iterative denoising of token blocks, achieving 2.42× the generation throughput of the autoregressive baseline while retaining 98.7% of benchmark quality.