Diffusion LLMs are a poor fit for distributed inference — here's the architecture-level reason
bartlomiej__ · reddit · 2026-10-27
b4rtaz analyzes why diffusion LLMs (bidirectional-attention denoisers like DiffusionGemma) don't distribute well: every denoising step computes over the full noisy canvas, so there's no KV-cache state to shard across devices the way causal-attention models enable via pipeline parallelism. The parallel advantage of diffusion LLMs is token-level parallelism on a single machine; across nodes, the global-canvas synchronization overhead outweighs the benefits, leaving autoregressive architectures the better fit for distributed inference. Includes an architecture diagram walking through the encoder, denoiser, and KV-cache interactions.
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