LLM inference is the process where a trained Large Language Model generates new text or predictions based on input data. This involves applying the model’s learned patterns to new prompts. It is the operational phase following model training, where the model performs its intended function. Efficient inference is crucial for real-time applications and user interaction.
Context
The efficiency and cost of LLM inference are central concerns in the development and deployment of artificial intelligence applications, including those within the crypto and Web3 space. Discussions focus on optimizing computational resources and reducing latency for practical use cases. Future developments will likely involve advancements in hardware acceleration, algorithmic improvements, and decentralized computing solutions to scale inference capabilities.
PolyLink introduces a blockchain-based platform enabling verifiable large language model inference at the edge, addressing centralization and ensuring computational integrity without substantial overhead.
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