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What is AI inference?

Quick Answer

AI inference is the process of using a trained model to generate outputs, whether predictions, classifications, text, or decisions, from new input data. It is the production phase of AI where models deliver value, as opposed to training where models learn. Inference costs, speed, and reliability are critical production concerns that directly affect your AI system's operational effectiveness and total cost of ownership.

Summary

Key takeaways

  • Inference is the production phase where trained models generate useful outputs
  • Speed, cost, and reliability are the key operational concerns for inference
  • Inference can run on cloud APIs, dedicated servers, or edge devices
  • Optimisation techniques can significantly reduce inference costs and latency

Understanding AI Inference

When you send a question to ChatGPT, ask an AI system to classify a document, or request a prediction from a machine learning model, you are running inference. The model takes your input, processes it through its learned parameters, and produces an output. For large language models, inference involves processing the input tokens through billions of parameters to generate each output token sequentially. The time this takes depends on the model size, hardware, and input/output length. For classification and prediction models, inference is typically much faster, often completing in milliseconds. Understanding inference is important because it determines the ongoing operational cost and performance characteristics of your AI system. While training happens once, inference happens with every user interaction or data processing job.

Optimising Inference for Production

Several techniques optimise inference performance and cost. Model quantisation reduces the precision of model weights, making models smaller and faster with minimal quality loss. Model distillation creates smaller models that mimic the behaviour of larger ones. Batching groups multiple requests together for more efficient processing. Caching stores common responses to avoid redundant computation. Hardware selection matters: GPUs are essential for large language models, while smaller models can run on CPUs. For cloud deployments, right-sizing your model to your task avoids paying for capability you do not need. Many organisations use a tiered approach, routing simple requests to small, fast models and complex requests to larger, more capable ones. This optimises both cost and user experience across different types of queries.

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FAQ

Frequently asked questions

Cloud API inference costs vary by model: GPT-4o costs approximately $2.50 per million input tokens, while smaller models cost 10-50x less. Local inference has no per-query cost but requires hardware investment. Volume discounts and model optimisation can significantly reduce costs.

Large language model inference typically takes 1 to 30 seconds depending on model size and output length. Classification and prediction models respond in milliseconds. Smaller models and optimised deployments reduce latency significantly.

Yes. Open-source models can run on your own GPUs using tools like vLLM, Ollama, or text-generation-inference. This provides data privacy and predictable costs at scale, though it requires hardware investment and technical management.

Training is the process of teaching a model by adjusting its parameters using large datasets, which is computationally expensive and done infrequently. Inference is the process of using the trained model to generate outputs from new inputs, which happens with every user interaction.

Yes. Techniques include caching frequent responses, batching requests, using model quantisation to reduce computation, implementing prompt compression, and routing simple queries to smaller models. These optimisations can reduce costs by 30-70% without affecting output quality.

Related Content

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Choosing models optimised for your inference needs

Local AI Deployment

Running inference on your own infrastructure

AI Cost Optimisation

Strategies to reduce AI operational costs

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