Quantized Models are machine learning models optimized for efficiency by reducing the precision of their numerical representations, typically from floating-point to lower-bit integer formats. This process significantly decreases computational requirements and memory footprint, making them suitable for resource-constrained environments. While reducing precision, these models aim to maintain a high level of accuracy for their intended tasks. They represent an advancement in deploying complex AI solutions more broadly.
Context
The application of Quantized Models is gaining traction in areas where computational resources are limited, such as edge devices or decentralized networks. Discussions in tech news often highlight their potential for enabling on-device AI in Web3 applications or for reducing the energy consumption of large language models. The ongoing research focuses on minimizing accuracy loss while maximizing efficiency gains through quantization techniques.
ZKPoT uses zk-SNARKs to verify decentralized model accuracy without revealing private data, solving the efficiency-privacy trade-off in federated learning.
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