NVIDIA quantized Google DeepMind's DiffusionGemma 26B A4B IT from 16-bit to 4-bit (NVFP4) using Model Optimizer, reducing memory requirements while maintaining benchmark performance within 1% of the full-precision baseline.
DiffusionGemma converts the existing Gemma-4-26B-A4B model into a text diffusion model using under 10% of the original training token budget, rather than training from scratch. It trades some quality on standard benchmarks for significantly higher generation speed and new self-correction capabilities.
Google releases DiffusionGemma 26B as open-weight model under Apache 2 license, bringing diffusion-based text generation to production with 500+ tokens/second inference speed.
Initial release of DiffusionGemma, a discrete diffusion-based text generation model built on Gemma 4 26B A4B MoE architecture with encoder-decoder design for parallel token generation.
Google DeepMind published a technical report on DiffusionGemma, a text diffusion model built by retrofitting Gemma-4-26B-A4B rather than training from scratch. The model generates 256-token blocks in parallel, reaches about 1,500 tokens per second on an Nvidia H100, and uses less than 10% of the original training budget.
NVIDIA released a quantized version of Google DeepMind's DiffusionGemma 26B A4B IT, a multimodal model with 25.2B total parameters (3.8B active) that processes text, image, and video inputs. The NVFP4-quantized model achieves generation speeds exceeding 1,100 tokens per second on NVIDIA H100 GPUs while supporting a 256K token context window.
Google has released DiffusionGemma 26B, an open-weight text generation model under Apache 2 license. The model generates over 500 tokens/second according to testing on NVIDIA's free NIM API, where it produced 2,409 tokens in 4.4 seconds.
Google DeepMind released DiffusionGemma, a 26B parameter mixture-of-experts model that generates text using discrete diffusion instead of autoregression. The model processes blocks of 256 tokens in parallel, achieving generation speeds exceeding 1100 tokens per second on H100 GPUs in low-batch settings.