Prefill vs Decode: LLM Inference Phases Explained

Large Language Model (LLM) inference involves two core phases: prefill and decode, each with unique characteristics and optimization needs. The prefill phase processes the entire input prompt in parallel, creating an internal state called the key-value (KV) cache, and is typically compute-bound, affecting the time to first token (TTFT). In contrast, the decode phase generates responses one token at a time, relying on the KV cache and is memory-bandwidth-bound, impacting inter-token latency (ITL). These phases interact differently with hardware and require distinct strategies for optimization, where prefill can be accelerated by efficient attention algorithms and semantic caching, while decode benefits from techniques like speculative decoding and quantization. The balance between these phases is crucial for ensuring responsiveness and cost-effectiveness, with the choice of optimization strategies depending on the specific demands of the application, such as whether it is more prefill-heavy, decode-heavy, or requires balanced performance across both phases. Redis offers solutions like semantic caching to address these challenges, potentially eliminating prefill and decode costs by leveraging real-time data platforms and vector search technology.

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