Boost inference performance for LLMs with new Amazon SageMaker containers

Boost inference performance for LLMs with new Amazon SageMaker containers AI News, AI, AI tools, AWS Machine Learning Blog, Innovation, itinai.com, LLM, Michael Nguyen, t.me/itinai 🚀 Boost Inference Performance for Large Language Models with Amazon SageMaker 🚀 Exciting news! Amazon SageMaker has just released a new version (0.25.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs) with added support for NVIDIA’s TensorRT-LLM Library. This update brings you state-of-the-art tools to optimize large language models (LLMs) on SageMaker and achieve significant price-performance benefits. 🔥 What's New with SageMaker LMI DLCs? 🔥 1️⃣ TensorRT-LLM Support: SageMaker now offers NVIDIA’s TensorRT-LLM as part of the latest LMI DLC release. This means you can leverage powerful optimizations like SmoothQuant, FP8, and continuous batching for LLMs when using NVIDIA GPUs. TensorRT-LLM significantly improves inference speed and supports deployments ranging from single-GPU to multi-GPU configurations. 2️⃣ Efficient Inference Collective Operations: SageMaker introduces a new collective operation that speeds up communication between GPUs in LLM deployments. This reduces latency and increases throughput with the latest LMI DLCs compared to previous versions. 3️⃣ Quantization Support: SageMaker LMI DLCs now support the latest quantization techniques, including GPTQ, AWQ, and SmoothQuant. These techniques optimize model weights, improve inference speed, and reduce memory footprint and computational cost while maintaining accuracy. 🔧 Using SageMaker LMI DLCs 🔧 Deploying your LLMs on SageMaker using the new LMI DLCs 0.25.0 is a breeze, with no changes required to your code. SageMaker LMI DLCs use DJL serving to serve your model for inference. Simply create a configuration file specifying settings like model parallelization and inference optimization libraries to use. 📊 Performance Benchmarking Results 📊 Performance benchmarks demonstrate significant improvements with the latest SageMaker LMI DLCs compared to previous versions. For example, latency reduced by 28-36% and throughput increased by 44-77% for a concurrency of 16. 📦 Recommended Configuration and Container 📦 SageMaker provides two containers: 0.25.0-deepspeed and 0.25.0-tensorrtllm. The DeepSpeed container contains DeepSpeed, the LMI Distributed Inference Library, while the TensorRT-LLM container includes NVIDIA’s TensorRT-LLM Library. These containers offer optimized deployment configurations for hosting LLMs. For more details on using SageMaker LMI DLCs and to explore practical AI solutions, visit [itinai.com](https://itinai.com). Discover how AI can redefine your sales processes and customer engagement with the AI Sales Bot from [itinai.com/aisalesbot](https://itinai.com/aisalesbot). 🔗 List of Useful Links 🔗 🔹 AI Lab in Telegram [@aiscrumbot](https://t.me/aiscrumbot) – free consultation 🔹 [Boost inference performance for LLMs with new Amazon SageMaker containers](https://aws.amazon.com/blogs/machine-learning/boost-inference-performance-for-large-language-models-with-new-amazon-sagemaker-containers/) 🔹 [AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/) 🔹 Twitter – [@itinaicom](https://twitter.com/itinaicom)