Achieving Efficient Generative AI Inference with Amazon SageMaker

As the demand for generative AI models continues to grow, so does the need for efficient inference methods. Amazon SageMaker has recently announced a new inference optimization toolkit that can help reduce costs by up to 50% and increase throughput by up to 2x for generative AI models.

The toolkit provides a menu of optimization techniques that can be applied to generative AI models, including speculative decoding, quantization, and compilation. By employing these techniques, developers can achieve best-in-class performance for their use cases while reducing deployment costs.

Optimizing generative AI models for efficient inference

Benefits of the Inference Optimization Toolkit

The new toolkit from Amazon SageMaker addresses the challenges of optimizing generative AI models for efficient inference. With the ability to select from a menu of latest model optimization techniques, developers can apply them to their models and evaluate the impact on output quality and inference performance in just a few clicks.

“Large language models require expensive GPU-based instances for hosting, so achieving a substantial cost reduction is immensely valuable,” said FNU Imran, Machine Learning Engineer, Qualtrics. “With the new inference optimization toolkit from Amazon SageMaker, based on our experimentation, we expect to reduce deployment costs of our self-hosted LLMs by roughly 30% and to reduce latency by up to 25% for up to 8 concurrent requests.”

Speculative Decoding

Speculative decoding is an inference technique that aims to speed up the decoding process of large and therefore slow language models for latency-critical applications without compromising the quality of the generated text. By using a smaller, less powerful, but faster language model called the draft model to generate candidate tokens that are then validated by the larger, more powerful, but slower target model, speculative decoding can significantly reduce the overall runtime.

Speculative decoding for efficient language model inference

Quantization

Quantization is one of the most popular model compression methods to reduce memory footprint and accelerate inference. By using a lower-precision data type to represent weights and activations, quantizing LLM weights for inference provides four main benefits: reduced hardware requirements for model serving, increased space for the KV cache, faster decoding latency, and a higher compute-to-memory access ratio.

Compilation

Compilation optimizes the model to extract the best available performance on the chosen hardware type, without any loss in accuracy. The SageMaker inference optimization toolkit provides efficient loading and caching of optimized models to reduce model loading and auto scaling time by up to 40-60% for Llama 3 8B and 70B.

Conclusion

The Amazon SageMaker inference optimization toolkit is a game-changer for generative AI inference. With its ability to reduce costs and increase throughput, developers can now deploy their generative AI models more efficiently. For more information on getting started with the inference optimization toolkit, refer to the user guide.