AI Inference

The process of running a trained artificial intelligence model to generate predictions or outputs in response to new inputs, as distinct from training, which is the computationally intensive process of learning model parameters from data. During inference, a trained model's fixed weights are applied to an input prompt, image, or data sample to produce an output, whether that is text, a classification label, a generated image, or an action decision. Inference is generally much less computationally expensive per query than training, but at scale across millions of users it represents the majority of ongoing compute cost for deployed AI systems. Inference efficiency is shaped by model size, hardware choices, quantization (reducing numerical precision), batching strategies, and specialized inference optimizations. The rise of test-time compute methods, where models reason through problems with extended chain-of-thought before answering, has increased inference costs for frontier models significantly. Decentralized GPU networks specifically target the inference market as an entry point because inference workloads are more latency-sensitive but more parallelizable and interruptible than training runs. Example: Nosana, a Solana-based decentralized GPU network launched in 2024, focused specifically on AI inference workloads rather than training. It allows GPU providers to serve inference requests for AI models including open-source LLMs, targeting developers and startups who need burst capacity for inference without committing to long-term cloud contracts. Why it matters for AI: As AI model deployment scales from millions to billions of daily interactions, inference efficiency becomes the dominant cost driver in AI economics. The economics of inference also shape which models can be deployed profitably and at what latency, influencing whether powerful AI remains concentrated in hyperscaler environments or can be made accessible through distributed infrastructure.

Explore the full Web3 Glossary — 2,000+ expert-curated definitions. Need guidance? Talk to our consultants.