Verified80% confidenceFactExact time
Large language model inference is divided into two phases, prefill and decode: prefill is compute-intensive and sensitive to GPU processing power, while decode is a memory-bandwidth-bound task.
7
Sources
80%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
AMD Ryzen AI Halo Hands-On: Can x86 Architecture Challenge NVIDIA's DGX Spark?
bilibili量子菠萝_7/7/2026
Related Claims
UnverifiedGPU utilization during the decoding phase of large language model inference is far lower than during the prefill phase's parallel matrix computation, because decoding is a step-by-step autoregressive generation process79% similarUnverifiedLLM inference requests are divided into two phases—Prefill (input) and Decode (output); Prefill can read inputs in parallel with extremely high throughput, while Decode requires token-by-token serial inference and occupies significantly more GPU time.77% similarUnverifiedThe inference process of large language models is divided into two phases, Prefill and Decode, and the computational complexity of the attention mechanism is O(n²).77% similarUnverifiedAt the hardware level, the Prefill (input) stage can read all tokens in parallel and compute the self-attention matrix simultaneously, whereas the Decode (output) stage requires serial autoregressive token-by-token inference, causing GPU utilization to drop sharply.76% similarVerified大语言模型的推理过程本质上是内存带宽受限（memory-bandwidth bound）的任务，每生成一个Token都需要将模型的全部权重从存储中读取一遍75% similar
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