Inference Server
Specialized hardware and software optimized for running trained AI models to generate predictions and responses, designed for high throughput and low latency in production environments.
Inference servers handle the majority of AI compute demand — while training is a one-time cost, inference runs continuously as millions of users interact with AI systems. NVIDIA's TensorRT-LLM and vLLM are leading inference optimization frameworks. Dedicated inference chips like AWS Inferentia and Groq LPUs offer 2-5x cost advantages over training-oriented GPUs for serving models. Inference server design must balance throughput (queries per second), latency (response time), and cost. As AI scales to billions of daily queries, inference infrastructure becomes the dominant cost and energy consumer.
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Related Terms
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
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