Amazon Web Services and Hugging Face have announced the launch of a new Deep Learning Container (DLC) for inference with Large Language Models (LLMs). This new offering is powered by Text Generation Inference (TGI), an open-source solution designed for deploying and serving LLMs. It brings significant optimizations such as tensor parallelism, dynamic batching, and model quantization to facilitate LLM deployment at scale. AWS customers can leverage this DLC on Amazon SageMaker for hosting LLMs like GPT-NeoX, StarCoder, and T5, among others, with advanced capabilities like autoscaling, health checks, and model monitoring.
Facts
🤝 AWS and Hugging Face are partnering to launch a new Deep Learning Container (DLC) for Large Language Models (LLMs) inference.
🚀 The new Hugging Face LLM DLC uses Text Generation Inference (TGI), a purpose-built open-source solution for deploying and serving LLMs.
💻 TGI incorporates several optimizations, such as tensor parallelism for faster multi-GPU inference, dynamic batching for improved throughput, and flash-attention for popular model architectures.
🚀 AWS customers can use Hugging Face LLM Inference DLCs on Amazon SageMaker to enjoy features like autoscaling, health checks, and model monitoring.
💡 The new service simplifies deploying and hosting LLMs like GPT-NeoX, StarCoder, BLOOM, GPT-NeoX, StableLM, Llama, and T5 at scale.
🛠️ The post provides a code example to deploy a GPT NeoX 20B parameter model on a SageMaker Endpoint.
📚 Resources to learn more about Hugging Face LLM Inference on SageMaker include an example notebook, Hugging Face TGI Repository, and Hugging Face TGI Launch Blog.