Google gives local LLMs a printing press upgrade
Google just open-sourced DiffusionGemma, an experimental 26B MoE model that ditches token-by-token generation for text diffusion, stamping out 256 tokens in parallel. It delivers up to 4x faster GPU inference, clocking over 1,000 tokens per second on an H100 and fitting into 18GB VRAM when quantized. Why it matters: It drastically cuts latency for local inline editing and code infilling, though Google notes raw output quality still trails standard Gemma 4.