Gemma 4

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.

Samsung's Galaxy Z Fold 8, Fold 8 Ultra, and Flip 8 are the first devices to ship with Google's Gemini Nano 4 on-device model and the new Gemini Intelligence feature tier. The launch comes with strict hardware requirements including 12GB+ RAM and qualified system-on-chips.

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 DeepMind has released quantization-aware training (QAT) optimized versions of its Gemma 4 model family in GGUF Q4_0 format. The QAT versions preserve similar quality to bfloat16 while dramatically reducing memory requirements, with models available across the entire Gemma 4 lineup: E2B, E4B, 12B, 26B A4B, and 31B.