Descriptions:
Fahd Mirza demonstrates how to run DiffusionGemma — Google’s new diffusion-based text generation model — locally using a quantized GGUF version released by Unsloth. Unlike every standard language model that generates tokens sequentially, DiffusionGemma drafts an entire block of 256 tokens simultaneously and iteratively denoises them in parallel, borrowing the architectural approach of image diffusion models for text generation.
The installation requires cloning a specialized fork of llama.cpp (PR #2423) rather than the standard repo, since the upstream llama.cpp has no diffusion runner. Mirza builds the dedicated llama-diffusion CLI binary on Ubuntu — a process that takes over an hour across six CPU cores — then downloads the Unsloth Q4 KM quantized model (16.8GB, 4-bit K-method medium quality). Running on an NVIDIA RTX 36000 with 48GB VRAM, the quantized model loads in just under 22GB, making it feasible on high-end prosumer hardware. Inference details include adaptive entropy-bound stopping (41 of 48 denoising steps in testing), a visible reasoning/thinking channel, and block-autoregressive processing for outputs longer than 256 tokens.
Quality comparison between the Q4 quantized and full-precision versions reveals meaningful degradation on a complex HTML coding task — the full-precision output was significantly richer. Image input is confirmed unsupported in the current CLI build. For practitioners wanting hands-on experience with diffusion-based text generation before cloud APIs stabilize, this is a practical step-by-step guide with honest performance caveats.
📺 Source: Fahd Mirza · Published June 11, 2026
🏷️ Format: Tutorial Demo
