Deploying quantized models on Amazon SageMaker AI with Unsloth | Amazon Web Services
This post was co-written with Daniel Han and Michael Han from Unsloth.
Deploying large foundation models (FMs) stored at their original 16-bit floating-point precision (BF16 or FP16) is expensive. They need large GPU instances, driving up serving costs, and slowing down iteration cycles. Quantization addresses this by reducing the numerical precision of a model’s weights (for example from 16-bit to 4-bit), which shrinks the memory usage significantly. The drawback of quantization is that it can reduce the accuracy of a model, which is where dynamic quantization becomes compelling. When done correctly, dynamic quantization can reduce memory usage while maintaining accuracy. The savings in instance cost, storage, and startup time can compound quickly at scale.
In this post, you will learn four deployment patterns for taking models that have already been quantized with Unslothand deploying them on AWS infrastructure. The patterns use Amazon Elastic Compute Cloud (Amazon EC2) for direct...
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