Inference (IA): definition and issues

L’inference In artificial intelligence means the process by which a previously trained model generates an answer to a user query. Unlike training, which mobilizes significant calculation resources over a long period, inference must be fast, efficient and repeated millions of times in production.

Why is inference crucial?

Inference is the step that Makes the AI ​​operational. Without it, a model cannot be used in real time. She plays a key role in many applications:

• Conversational assistants (eg chatgpt, mistral the cat)
• Automatic translation (Ex. Deeppl, Google Translate)
• Recognition of images and voice (Ex. Google Lens, Siri)
• Recommendation systems (Ex. Netflix, Spotify)

Technological issues

Inference is a bottleneck For AI companies due to three major constraints:

1. Response speed ⚡
   • An AI must generate results in a few milliseconds to provide a fluid experience.
   • Ex. Mistral the cat reaches 1,000 words per second Thanks to a partnership with Cerebras.
2. Energy cost and equipment 💰
   • Inference represents up to 90 % of operating costs of an AI model.
   • THE Nvidia gpu Dominate the market, but alternatives emerge (Cerebras, Google TPU, Amazon Trainium).
3. Model optimization 🏗️
   • Techniques used: quantity (Reduction of the accuracy of calculations), compression of models, light architectures.

Inference vs training: what difference?

The future of inference

• Model optimization To reduce costs.
• Material diversification with specialized fleas.
• AI democratization With lighter and accessible models.

Inference becomes a key differentiation factor for AI actors. Mastering this phase improves the speed, accessibility and profitability of models.