Google released DiffusionGemma, a 26B MoE experimental open model using text diffusion instead of token-by-token autoregressive decoding. It can generate blocks of text in parallel, reaching up to 4x faster output on dedicated GPUs. The model targets local, speed-sensitive workflows, but Google says its output quality is below standard Gemma 4 and recommends Gemma 4 for quality-critical production use.
The post’s title indicates a performance claim for real-time LLM inference on standard GPUs, reporting 3,000 tokens per second per request. No article body is available, so the underlying model, GPU type, batch size, latency profile, precision, serving stack, and benchmark method are not stated. The item is best treated as an inference-performance benchmark claim rather than a verified deployment guide.