SAM ALTMAN SAYS AGENTS ARE COMING • CHATGPT GAINED SENTIENCE FOR 4 SECONDS • GOOGLE RELEASES 40th LLM THIS WEEK • NVIDIA MARKET CAP EXCEEDS REALITY • ANTHROPIC ENGINEER DISCOVERS NEW FORM OF GRIEF • MISTRAL RAISES AT VALUATION OF GROSS DOMESTIC PRODUCT • SAM ALTMAN SAYS AGENTS ARE COMING • CHATGPT GAINED SENTIENCE FOR 4 SECONDS • GOOGLE RELEASES 40th LLM THIS WEEK • NVIDIA MARKET CAP EXCEEDS REALITY • ANTHROPIC ENGINEER DISCOVERS NEW FORM OF GRIEF • MISTRAL RAISES AT VALUATION OF GROSS DOMESTIC PRODUCT •
toolsWTF 5.4via r/LocalLLaMA

Benchmarked every spec-decode method on Qwen3.6-27B across vLLM and SGLang (single RTX PRO 6000 Max-Q)

"Local LLM math just dropped: DFlash is making 27B models run like they're on caffeine."

Explain Like I'm Normal

A community developer benchmarked various speculative decoding methods for Qwen 2.5/3.2 series on a single GPU to see which engine actually delivers speed. DFlash emerged as the winner, providing up to a 3.3x speedup on SGLang, significantly outperforming EAGLE and standard ngram approaches. This data helps builders choose the right inference stack to minimize latency without needing massive compute clusters.

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#inference#benchmarking#vllm#sglang#qwen

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