AWS scales AI with inference-focused systems
Inference-first design helps AWS reduce AI costs while supporting complex, large-scale applications worldwide.
AI assistants deliver answers in seconds, but the process behind the scenes, called inference, is complex. Inference lets trained AI models generate responses, recommendations, or images, accounting for up to 90% of AI computing power.
AWS has built infrastructure to handle these fast, high-volume operations reliably and efficiently.
Inference involves four main stages: tokenisation, prefill, decoding, and detokenisation. Each step converts human input into machine-readable tokens, builds context, generates responses token by token, and converts output back to readable text.
AWS custom Trainium chips speed up the process while reducing costs. AI agents add complexity by chaining multiple inferences for multi-step tasks.
AWS uses its Bedrock platform, Project Mantle engine, and Journal tool to manage long-running requests, prioritise urgent tasks, and maintain low latency. Unified networking ensures efficiency and fairness across users.
By focusing on inference-first infrastructure, AWS lowers AI costs while enabling more advanced applications. Instant responses from AI assistants are the result of years of engineering, billions in investment, and systems built to scale globally.
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