Deploy DiffusionGemma on GPU Cloud for 1,400+ Tokens Per Second

About this lesson

Deploy Google DeepMind's DiffusionGemma on the Hyperstack GPU cloud in this step-by-step tutorial. Learn how to run this high-speed diffusion language model on a single NVIDIA H100 using vLLM. DiffusionGemma is a Mixture-of-Experts model with 25.2B total parameters and just 3.8B activated during inference. It uses discrete diffusion to denoise a 256-token generation canvas per diffusion step in parallel, delivering 1,400+ tokens per second on a single NVIDIA H100 with the Entropy-Bounded sampler, roughly 4x faster than autoregressive models. In this tutorial, you'll learn: - What DiffusionGemma is and how its discrete diffusion architecture works - How to provision a single NVIDIA H100-80GB PCIe VM on Hyperstack - How to launch a vLLM inference server with the Entropy-Bounded diffusion sampler - How to query the OpenAI-compatible API for text generation use cases - How to benchmark real-world throughput on your own hardware The model supports a 256K token context window, multimodal input (text, image and video) and delivers 1,400+ tokens/sec on NVIDIA H100, ideal for high-volume chat, code generation and document processing workloads. Full tutorial on Hyperstack Blog: https://eu1.hubs.ly/H0wljyw0 Get started on Hyperstack: https://eu1.hubs.ly/H0wlfz_0 If this helped, like and subscribe for more GPU cloud and LLM deployment tutorials!

Original Description