The provided QbitAI title indicates that Google released a model quietly while attention was focused on Mythos. The only concrete performance claim available is that speed increased by 4x, but the model name, task scope, benchmark method, and availability are not provided. Based on the title alone, this appears to be a model-release item relevant to developers and AI practitioners tracking latency and throughput improvements.
Google has announced DiffusionGemma, a text-generation model that applies diffusion-based techniques to the Gemma architecture, claiming speeds four times faster than standard autoregressive generation. Unlike conventional language models that predict tokens one at a time, diffusion-based methods generate text through iterative denoising, enabling parallel output. The release, published on Google's official blog, drew immediate attention from the local-LLM community for its potential inference-efficiency gains.
A r/LocalLLaMA user shared quick throughput numbers for Gemma4 QAT with MTP speculative decoding on an RTX 3090 24GB setup. They report roughly 1.2-1.8x TPS improvement, with Gemma 4 31B moving from about 40 tok/s to 70-80 tok/s. The author frames this as a rough benchmark, using 11 task categories and noting stochastic variation from temp 1.0.