Descriptions:
This hands-on tutorial from the Fahd Mirza channel demonstrates running Google’s Gemma 4 31B model locally at 196 tokens per second using Red Hat’s newly released DFlash speculator on a single NVIDIA H100 80GB VRAM GPU — a result the host describes as faster than most people can read. The video marks a notable first: DFlash speculative decoding, previously confined to Qwen-family models, is now available for Google’s Gemma 4 architecture through an open-source release from Red Hat built on the vLLM Speculators library.
The tutorial explains the technical mechanism behind DFlash: rather than generating tokens sequentially, a small draft model examines the hidden states of the large base model and proposes an entire block of tokens in a single forward pass, which the base model then verifies all at once. This differs from standard speculative decoding in that drafting cost stays flat regardless of how many tokens are proposed, producing higher acceptance rates. The Speculators library standardizes training and packaging of these draft models in a Hugging Face-compatible format, deployable with a single vLLM serve command.
The video covers the complete local setup: downloading both the Gemma 4 31B base model and draft model automatically via vLLM, monitoring VRAM consumption, running a real inference benchmark (1,024 tokens generated in 5.1 seconds), and reading server-side speculative decoding metrics — including a mean acceptance length of 6.46, meaning the draft model correctly predicts roughly six consecutive tokens at a time. The result is a practical, reproducible guide for anyone looking to run large open-weight models at high throughput on local or rented GPU hardware.
📺 Source: Fahd Mirza · Published May 06, 2026
🏷️ Format: Tutorial Demo
