Make your llama generation time fly with AWS Inferentia2
This Hugging Face blog post covers deploying and optimizing Llama 2 inference on AWS Inferentia2 accelerators. It demonstrates integration between Hugging Face's Optimum Neuron library and AWS's custom silicon to achieve competitive inference throughput and latency. The post serves as a practical guide for enterprise teams looking to reduce inference costs by moving off GPU-based infrastructure.
Related guides (4)
Related events (8)
Accelerating Hugging Face Transformers with AWS Inferentia2
Hugging Face published a blog post detailing how to accelerate Transformer model inference using AWS Inferentia2, Amazon's second-generation ML inference chip. The post covers integration patterns between the Hugging Face ecosystem and the Neuron SDK for deploying models on Inferentia2 hardware. This represents a practical guide for enterprise and cloud-based inference deployment using dedicated AI accelerators.
Llama 2 on Amazon SageMaker: A Benchmark
This Hugging Face blog post benchmarks Llama 2 model inference on Amazon SageMaker, examining performance and cost characteristics across different instance types and configurations. The analysis provides practical guidance for deploying open-weights LLMs in cloud infrastructure. It covers throughput, latency, and cost trade-offs relevant to enterprise deployment decisions.
Deploy models on AWS Inferentia2 from Hugging Face
Hugging Face has announced support for deploying models on AWS Inferentia2 via Hugging Face Inference Endpoints. The integration allows users to deploy popular open-weight models on AWS's custom ML accelerator chips directly from the Hugging Face Hub. This expands the hardware options available for cost-effective inference beyond standard GPU instances.
Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia
This Hugging Face blog post describes how to deploy BERT models on AWS Inferentia chips using the Hugging Face Transformers library and Amazon SageMaker. It covers the workflow for compiling models with AWS Neuron SDK and running optimized inference on Inferentia hardware. The post targets practitioners looking to reduce inference costs and latency for transformer-based NLP workloads.
Hugging Face Text Generation Inference available for AWS Inferentia2
Hugging Face has announced that its Text Generation Inference (TGI) serving framework is now available for AWS Inferentia2 accelerators. This integration allows users to deploy large language models on AWS's custom AI chips using the TGI stack. The move extends TGI's hardware support beyond GPUs to specialized inference silicon, potentially offering cost and performance advantages for production LLM deployments.
Introducing the Hugging Face LLM Inference Container for Amazon SageMaker
Hugging Face and Amazon Web Services have launched a dedicated LLM inference container for Amazon SageMaker, enabling optimized deployment of large language models on managed cloud infrastructure. The container is built on Hugging Face's Text Generation Inference (TGI) toolkit, which supports features like continuous batching, tensor parallelism, and quantization. This integration lowers the barrier for enterprise teams to deploy open-weight LLMs at scale on AWS without managing custom serving infrastructure.
Deploy LLMs with Hugging Face Inference Endpoints
Hugging Face published a guide on deploying large language models using their Inference Endpoints service. The post covers how to set up scalable, production-ready LLM deployments with minimal infrastructure overhead. It targets developers looking to move from experimentation to hosted inference without managing raw compute.
Accelerate a World of LLMs on Hugging Face with NVIDIA NIM
NVIDIA NIM microservices are being integrated with Hugging Face to enable optimized inference deployment for a broad range of LLMs hosted on the Hub. The partnership allows developers to deploy Hugging Face models via NIM's containerized inference stack, leveraging NVIDIA's TensorRT-LLM and other optimizations. This expands the ecosystem of models accessible through NIM beyond NVIDIA's own catalog to the wider Hugging Face model repository.