Amazon SageMaker has released a new inference optimization toolkit, which significantly shortens the time it takes to enhance generative artificial intelligence (AI) models. The toolkit offers several optimization techniques that can be applied to AI models and validated in just a few simple steps, ultimately reducing costs and boosting performance. It uses methods such as speculative decoding, quantization, and compilation, promising up to double the throughput and about half the cost for certain generative AI models like Llama 3, Mistral, and Mixtral.
In the past, implementing AI optimization techniques required months of developer time due to extensive research and experimentation. The toolkit drastically simplifies this process by offering a variety of pre-existing model optimization techniques that can be applied to models at the user’s convenience. Users can customize an optimization recipe for their models, benchmark the effects of each optimization technique on their data, and deploy popular models within minutes.
One key technique applied in this toolkit includes speculative decoding, which predicts and computes multiple potential next tokens simultaneously to achieve faster results without compromising accuracy. Quantization reduces the memory requirements of a model by representing its weights and activations in a lower-precision data type, while compilation tailors the model to deliver the best performance on specific hardware.
The toolkit also enables the use of Activation-aware Weight Quantization (AWQ) for GPUs, a technique that compresses model weights for faster decoding latency, less expensive hardware requirements, and reduced data transfer. AWQ quantizes such weights to INT4, effectively reducing the memory footprint of weights to one-fourth their original size.
Furthermore, the toolkit facilitates better model performance through efficient loading and caching of optimized models. This considerably diminishes model loading and auto scaling time.
The toolkit also offers ahead-of-time compilation and a cache of pre-compiled artifacts, permitting faster deployment of models onto accelerated hardware, such as GPUs or devices like AWS Trainium and AWS Inferentia. SageMaker automatically uses these pre-compiled artifacts when configurations match, effectively avoiding time-consuming recompilations.
Finally, Amazon SageMaker’s inference optimization toolkit provides an easy and efficient solution for businesses to improve their AI models by eliminating the need for heavy research and developer resources associated with the optimization process. This streamlined process allows businesses to concentrate more on their objectives, drastically reducing time and costs, and providing best-in-class performance.
