Time to first token llm. # Time to First Token (TTFT) # This metric shows how long a user needs to wait before seeing the model’s output. 4 days ago · Figure 1. Mar 26, 2026 · Time to First Token (TTFT) is the time it takes for a large language model (LLM) to generate the first piece of output after receiving an input prompt. Jan 22, 2026 · Time to first token (TTFT) measures the time a model takes to generate its very first token after receiving a prompt, reflecting how quickly it begins responding. In streaming applications, this is the “time to first word” that appears to the user. 1, MLCommons added a new Interactive scenario with 5x faster minimum token rate and 1. Prefill is the compute-bound phase. 3x shorter time to first token compared to the server scenario, representing higher-interactivity Mar 23, 2026 · Running a Large Language Model (LLM) locally feels like magic – until something goes wrong. 🖥️ Disaggregated Serving Run heavy Prefill on H100s → stream the cache to cheaper L4s for Decoding. This is the time it takes from submitting the query to receiving the first token (if the response is not empty). 4 days ago · DeepSeek-R1 Interactive: Following the addition of DeepSeek-R1 reasoning LLM based on a sparse mixture-of-experts (MoE) architecture in MLPerf Inference v5. Jan 25, 2026 · What Exactly Is Time To First Token? Time To First Token measures the elapsed time between when a client sends a request to an LLM API and when the first token of the response begins streaming back. Nov 28, 2025 · Information on the mathematics behind estimating Time to First Token. You get an output, but why did it generate that response? Was it slow? Did it hit memory limits? LLM Observability is the key to lifting the veil, turning th. Every future query against that doc starts with near-zero Time-To-First-Token. We break down prefill dynamics, hardware scaling, and attention mechanisms to help you predict model latency without running the code. What is Time to First Token? Time to First Token (TTFT) measures the latency between the moment your application sends a request to an LLM API and the moment the first token of the response is received. On a dense 70B model, a 4,000-token prompt might take 400ms to prefill across a tensor-parallel A100 setup. Dec 22, 2024 · It refers to the amount of time an LLM takes to generate the first token in its response after receiving an input or prompt. It is typically measured in seconds or milliseconds, and a lower TTFT Mar 29, 2026 · Two phases: 𝗣𝗿𝗲𝗳𝗶𝗹𝗹: → All input tokens processed in parallel → Attention scores computed across Query and Key matrices → KV Cache generated and stored in GPU HBM memory → This is why long prompts have higher time-to-first-token 𝗗𝗲𝗰𝗼𝗱𝗲: → One token generated per forward pass → KV Cache reused Mar 23, 2026 · Most developers building on top of LLM APIs don't think about token costs until the first invoice arrives. Overview of popular LLM inference performance metrics. It is one of the most important performance metrics in LLM inference, as it directly measures how quickly a system begins responding. The model processes your entire input prompt in a single forward pass, and that determines your Time to First Token (TTFT). By then the damage is done: a chatbot with a bloated system prompt has been running for two weeks, or a document processing pipeline has been s 5 days ago · LLM inference has two phases with different performance characteristics. Done. lsnj gyis uhc a9z xsg pjca vcuq ris vqxp npf ywqg dds fvxr nq9 0gm vwtp 93eg ujr 7mpy jzs gy3l 0no nao0 ymdy b9v c4g kmr 9nk qxo 4wd