What is inference?

Inference is the process of using a trained AI model to make predictions or generate outputs. When you ask an AI a question and get an answer, that's inference.

Inference is when you use an already-trained AI model to process new inputs and produce outputs. The model has already learned from training data; inference is applying that knowledge.

[Training]: Teaching the model (happens once, takes a long time) [Inference]: Using the model (happens every time you make a request, fast)

1. ▸[You provide input]: Send a prompt or question to the model
2. ▸[Model processes]: The model uses its learned patterns to understand your input
3. ▸[Model generates output]: The model produces a response based on its training
4. ▸[You receive result]: Get the AI's generated text, prediction, or answer

[Training]:

• ▸Happens once (or periodically)
• ▸Takes days or weeks
• ▸Requires massive computational resources
• ▸Expensive
• ▸Creates the model

[Inference]:

• ▸Happens every request
• ▸Takes seconds or milliseconds
• ▸Requires less computation
• ▸Relatively inexpensive per request
• ▸Uses the trained model

[Model size]: Larger models are slower but more capable [Input length]: Longer prompts take more time to process [Output length]: Generating more text takes more time [Hardware]: Better hardware (GPUs) speeds up inference [Provider infrastructure]: Cloud providers optimize for speed

[Latency]: How long it takes to get a response

• ▸[Fast models]: Respond in milliseconds (GPT-3.5 Turbo)
• ▸[Slower models]: Can take several seconds (GPT-4)

[Throughput]: How many requests can be processed per second

• ▸Depends on model, hardware, and optimization

[Model choice]: Use faster models when speed matters more than capability [Prompt length]: Shorter prompts process faster [Caching]: Cache common responses to avoid repeated inference [Batching]: Process multiple requests together for efficiency [Hardware]: Use GPUs or specialized AI chips for faster inference

Inference costs depend on:

• ▸[Tokens processed]: Both input and output tokens
• ▸[Model used]: More capable models cost more
• ▸[Provider]: Different providers have different pricing
• ▸[Volume]: Higher usage may get discounts

[Latency requirements]: Some applications need fast responses (chatbots), others can wait (email generation)

[Cost at scale]: Inference costs can add up quickly with high volume

[Reliability]: Inference services need to be available when you need them

[Rate limits]: Providers limit how many requests you can make

Understanding inference helps you make better decisions about which models to use and how to optimize your AI applications.