Boost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding - NVIDIA Developer
NVIDIA Developer publishes the DFlash speculative-decoding technique, claiming up to 15x inference throughput gains on Blackwell GPUs.
2 items · 2 sources · 2 days
Operational story trace
Follow in this browser to see new updates on your Live feed.
Latest change
Modal named DFlash as the technique behind its own state-of-the-art inference-latency results, working with Z Lab and SGLang to release open-source pre-trained speculator models — including one for Qwen that improves over multi-token prediction by more than 50%.
Speculative decoding — drafting candidate tokens ahead of the target model to cut serving latency — is a known lever for LLM inference cost. NVIDIA's June 23 developer post introduced DFlash, a speculator built on the target model's own KV projections, claiming up to 15x throughput gains on Blackwell GPUs.
Arc
NVIDIA Developer publishes the DFlash speculative-decoding technique, claiming up to 15x inference throughput gains on Blackwell GPUs.
Modal names DFlash as the technique behind its own state-of-the-art latency results, releasing open-source speculator models built with Z Lab and SGLang.
NVIDIA Developer publishes the DFlash speculative-decoding technique, claiming up to 15x inference throughput gains on Blackwell GPUs.
Modal names DFlash as the technique behind its own state-of-the-art latency results, releasing open-source speculator models built with Z Lab and SGLang.
What to watch — open questions
Storylines are threaded mechanically from the feed: stories that share a distinctive anchor across multiple days and sources. Each item links to its original source. The evidence trace, current state, and open questions are written by the editor routine and refreshed whenever a new beat lands.