"The battle to win on AI inference, of course, is over its economics. Once a model is trained, every useful thing it does-answering a query, generating code, recommending a product, summarizing a document, powering a chatbot, or analyzing an image-happens during inference. That's the moment AI goes from a sunk cost into a revenue-generating service, with all the accompanying pressure to reduce costs, shrink latency (how long you have to wait for an AI to answer), and improve efficiency."
"Nvidia CEO Jensen Huang has been explicit about the challenge of inference. While he says Nvidia is "excellent at every phase of AI," he told analysts at the company's Q3 earnings call in November that inference is "really, really hard." Far from a simple case of one prompt in and one answer out, modern inference must support ongoing reasoning, millions of concurrent users, guaranteed low latency, and relentless cost constraints."
Nvidia invested $20 billion to license Groq's technology and hired most of Groq's team, including its founder. Inference transforms trained models into revenue-generating services, handling queries, code generation, recommendations, summarization, chatbots, and image analysis. Inference economics drive industry focus because every useful model action occurs during inference, creating pressure to cut costs, reduce latency, and increase efficiency. Modern inference must support ongoing multi-step reasoning, millions of concurrent users, guaranteed low latency, and strict cost constraints. Specialized chips optimized for fast, low-latency inference aim to address these demands beyond general-purpose GPUs.
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