7 Approaches to Reduce Inference Latency in Your LLM Workflows
# Dealing With Inference Latency As large language models (LLMs) move from research prototypes into production, engineering teams run into a hard truth: building an intelligent model is only half the battle. Serving that model to users in real time is a different engineering challenge entirely. In generative AI, inference is the phase where a trained model processes your input (the prompt) and generates an output (the response).
- ▪# Dealing With Inference Latency As large language models (LLMs) move from research prototypes into production, engineering teams run into a hard truth: building an intelligent model is only half the battle.
- ▪Serving that model to users in real time is a different engineering challenge entirely.
- ▪In generative AI, inference is the phase where a trained model processes your input (the prompt) and generates an output (the response).
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# Dealing With Inference Latency As large language models (LLMs) move from research prototypes into production, engineering teams run into a hard truth: building an intelligent model is only half the battle. Serving that model to users in real time is a different engineering challenge entirely. In generative AI, inference is the phase where a trained model processes your input (the prompt) and generates an output (the response). Inference latency is the time delay during this process. Unlike standard web applications where latency is usually measured in milliseconds, LLM latency can stretch into seconds or longer if left unoptimized, leading to poor user experiences and high compute costs. Understanding the anatomy of a slow response is the first step.
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