The Kuleshov Group published a full build guide for diffusion language models.

Adapted from ICLR 2026 and MLSS 2026 workshops, covering masked diffusion, block diffusion for variable-length generation, encoder-decoder architectures, remasking-based error correction, sampling distillation, guidance and RL post-training (Kuleshov Group). It catalogs what you can actually build on: LLaDA at 8B with open weights and LLaMA compatibility, Google's Gemma Diffusion, NVIDIA's Nemotron Diffusion family up to 35B, ESM3 at 100B for proteins, Nucleotide Transformer v3 on about a trillion DNA tokens. The commercial datapoint is Mercury 2 at 1,000+ tokens/second on standard GPUs, which the authors put at 5-10x comparable-quality models.