Cerebras Explains | What is the difference between latency and throughput in AI?

What is the difference between Latency and Throughput in AI? 🧠 How it works: Latency and throughput measure two distinct sides of AI performance. Latency is the response time—how long it takes from sending a prompt to receiving a result (including Time to First Token and inter-token latency). Throughput, on the other hand, measures overall system capacity—how many total tokens, requests, or queries the hardware can process per second across all users. 🚀 Why speed matters: Optimizing only for throughput often leads to high batching and sluggish latency, while focusing solely on latency can leave compute resources underutilized. For production AI applications—from interactive voice agents to large-scale enterprise workflows—understanding and optimizing both latency and throughput ensures users get instant, real-time responses while maintaining maximum infrastructure efficiency at scale. 🔔 Subscribe to Cerebras for more deep dives in our "What Is..." AI series! 👉 https://www.youtube.com/@CerebrasSystems?sub_confirmation=1 🔗 Learn more at: https://www.cerebras.ai/glossary/latency-vs-throughput-in-ai 💬 What AI topic or developer tool should we break down next? Let us know in the comments! 👇 #Cerebras #LatencyVsThroughput #AIInference #Throughput #Latency #ArtificialIntelligence #MachineLearning #LLM #SoftwareEngineering #TechExplainer #Shorts