Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face

Fetch Consolidates AI Tools and Saves 30% Development Time with Hugging Face on AWS

Introducing the Hugging Face LLM Inference Container for Amazon SageMaker

The Partnership: Amazon SageMaker and Hugging Face

Introducing the Hugging Face Embedding Container for Amazon SageMaker

Llama 2 on Amazon SageMaker a Benchmark

Deploy Hugging Face models easily with Amazon SageMaker

From Hugging Face to Amazon SageMaker Studio in one click

Case Study: Millisecond Latency using Hugging Face Infinity and modern CPUs

Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model

Enterprise Document Intelligence [Vol. 1 #9ter] - The pipeline from Article 9 calls a model at several steps to be sure it is right.

Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker

UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpods

arXiv:2607. 06202v1 Announce Type: cross Abstract: The deployment of Mixture-of-Experts (MoE) models on production high-bandwidth superpods, such as NVIDIA's NVL72/576 and Huawei's CloudMatrix384, introduces critical challenges beyond raw interconnect bandwidth.

Hugging Face Hub on the AWS Marketplace: Pay with your AWS Account