•1 min read•from Towards Data Science
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash
Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acceptance metrics, and show what determines whether speculation pays off.
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Tagged with
#speculative decoding
#token generation
#CPUs
#DFlash
#autoregressive throughput
#vLLM
#Qwen3.5-9B
#Intel Xeon 6
#concurrency
#acceptance metrics
#compute
#model output
#speedup
#machine learning
#natural language processing
#deep learning
#performance optimization
#throughput
#language models
#generative AI