Amazon EC2 Inf1 Instances

AWS Inferentia is an ML chip purpose built by AWS to deliver high-performance inference at low cost. Each AWS Inferentia chip has four first-generation NeuronCores and provides up to 128 tera operations per second (TOPS) of performance and support for FP16, BF16, and INT8 data types. AWS Inferentia chips also feature a large amount of on-chip memory that can be used for caching large models, which is especially beneficial for models that require frequent memory access.

The AWS Neuron SDK consists of a compiler, runtime driver, and profiling tools. It enables deployment of complex neural net models, created and trained in popular frameworks such as TensorFlow, PyTorch, and MXNet, to be executed using Inf1 instances. With NeuronCore Pipeline, you can split large models for execution across multiple Inferentia chips using a high-speed physical chip-to-chip interconnect, delivering high inference throughput and lower inference costs.

Inf1 instances offer up to 100 Gbps of networking throughput for applications that require access to high-speed networking. Next-generation Elastic Network Adapter (ENA) and NVM Express (NVMe) technology provide Inf1 instances with high-throughput, low-latency interfaces for networking and Amazon Elastic Block Store (Amazon EBS).

The AWS Nitro System is a rich collection of building blocks that offloads many of the traditional virtualization functions to dedicated hardware and software to deliver high performance, high availability, and high security while also reducing virtualization overhead.

Founded in 2008, San Francisco–based Airbnb is a community marketplace with over 4 million hosts who have welcomed more than 900 million guest arrivals in almost every country across the globe.

"Airbnb’s Community Support Platform enables intelligent, scalable, and exceptional service experiences to our community of millions of guests and hosts around the world. We are constantly looking for ways to improve the performance of our NLP models that our support chatbot applications use. With Amazon EC2 Inf1 instances powered by AWS Inferentia, we see a 2x improvement in throughput out of the box over GPU-based instances for our PyTorch-based BERT models. We look forward to leveraging Inf1 instances for other models and use cases in the future."

Bo Zeng, Engineering Manager, Airbnb

"We incorporate ML into many aspects of Snapchat, and exploring innovation in this field is a key priority. Once we heard about Inferentia, we started collaborating with AWS to adopt Inf1/Inferentia instances to help us with ML deployment, including around performance and cost. We started with our recommendation models and look forward to adopting more models with the Inf1 instances in the future."

Nima Khajehnouri, VP Engineering, Snap Inc.

"Sprinklr's AI-driven unified customer experience management (Unified-CXM) platform enables companies to gather and translate real-time customer feedback across multiple channels into actionable insights—resulting in proactive issue resolution, enhanced product development, improved content marketing, better customer service, and more. Using Amazon EC2 Inf1, we were able to significantly improve the performance of one of our NLP models and improve the performance of one of our computer vision models. We're looking forward to continuing to use Amazon EC2 Inf1 to better serve our global customers."

Vasant Srinivasan, Senior Vice President of Product Engineering, Sprinklr

“Our state-of-the-art NLP product, Finch for Text, offers users the ability to extract, disambiguate, and enrich multiple types of entities in huge volumes of text. Finch for Text requires significant computing resources to provide our clients with low-latency enrichments on global data feeds. We are now using AWS Inf1 instances in our PyTorch NLP, translation, and entity disambiguation models. We were able to reduce our inference costs by over 80% (over GPUs) with minimal optimizations while maintaining our inference speed and performance. This improvement allows our customers to enrich their French, Spanish, German, and Dutch language text in real time on streaming data feeds and at global scale—something that’s critical for our financial services, data aggregator, and public sector customers."

Scott Lightner, Chief Technology Officer, Finch Computing

"We alert on many types of events all over the world in many languages, in different formats (images, video, audio, text sensors, combinations of all these types) from hundreds of thousands of sources. Optimizing for speed and cost given that scale is absolutely critical for our business. With AWS Inferentia, we have lowered model latency and achieved up to 9x better throughput per dollar. This has allowed us to increase model accuracy and grow our platform's capabilities by deploying more sophisticated DL models and processing 5x more data volume while keeping our costs under control."

Alex Jaimes, Chief Scientist and Senior Vice President of AI, Dataminr

"Autodesk is advancing the cognitive technology of our AI-powered virtual assistant, Autodesk Virtual Agent (AVA), by using Inferentia. AVA answers over 100,000 customer questions per month by applying natural language understanding (NLU) and deep learning (DL) techniques to extract the context, intent, and meaning behind inquiries. Piloting Inferentia, we are able to obtain a 4.9x higher throughput over G4dn for our NLU models, and look forward to running more workloads on the Inferentia-based Inf1 instances."

Binghui Ouyang, Sr. Data Scientist, Autodesk

Amazon Advertising helps businesses of all sizes connect with customers at every stage of their shopping journey. Millions of ads, including text and images, are moderated, classified, and served for the optimal customer experience every single day.

“For our text ad processing, we deploy PyTorch based BERT models globally on AWS Inferentia based Inf1 instances. By moving to Inferentia from GPUs, we were able to lower our cost by 69% with comparable performance. Compiling and testing our models for AWS Inferentia took less than three weeks. Using Amazon SageMaker to deploy our models to Inf1 instances ensured our deployment was scalable and easy to manage. When I first analyzed the compiled models, the performance with AWS Inferentia was so impressive that I actually had to re-run the benchmarks to make sure they were correct! Going forward, we plan to migrate our image ad processing models to Inferentia. We have already benchmarked 30% lower latency and 71% cost savings over comparable GPU-based instances for these models."

Yashal Kanungo, Applied Scientist, Amazon Advertising

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“Amazon Alexa’s AI- and ML-based intelligence, powered by AWS, is available on more than 100 million devices today—and our promise to customers is that Alexa is always becoming smarter, more conversational, more proactive, and even more delightful. Delivering on that promise requires continuous improvements in response times and ML infrastructure costs, which is why we are excited to use Amazon EC2 Inf1 to lower inference latency and cost per inference on Alexa text-to-speech. With Amazon EC2 Inf1, we’ll be able to make the service even better for the tens of millions of customers who use Alexa each month."

Tom Taylor, Senior Vice President, Amazon Alexa

"We are constantly innovating to further improve our customer experience and to drive down our infrastructure costs. Moving our web-based question answering (WBQA) workloads from GPU-based P3 instances to AWS Inferentia-based Inf1 instances not only helped us reduce inference costs by 60%, but also improved the end-to-end latency by more than 40%, helping enhance customer Q&A experience with Alexa. Using Amazon SageMaker for our TensorFlow-based model made the process of switching to Inf1 instances straightforward and easy to manage. We are now using Inf1 instances globally to run these WBQA workloads and are optimizing their performance for AWS Inferentia to further reduce cost and latency."

Eric Lind, Software Development Engineer, Alexa AI

“Amazon Prime Video uses computer vision ML models to analyze video quality of live events to ensure an optimal viewer experience for Prime Video members. We deployed our image classification ML models on EC2 Inf1 instances and were able to see 4x improvement in performance and up to 40% savings in cost. We are now looking to leverage these cost savings to innovate and build advanced models that can detect more complex defects, such as synchronization gaps between audio and video files, to deliver more enhanced viewing experience for Prime Video members."

Victor Antonino, Solutions Architect, Amazon Prime Video

“Amazon Rekognition is a simple and easy image and video analysis application that helps customers identify objects, people, text, and activities. Amazon Rekognition needs high-performance DL infrastructure that can analyze billions of images and videos daily for our customers. With AWS Inferentia-based Inf1 instances, running Amazon Rekognition models such as object classification resulted in 8x lower latency and 2x the throughput compared to running these models on GPUs. Based on these results, we are moving Amazon Rekognition to Inf1, enabling our customers to get accurate results faster."

Rajneesh Singh, Director, SW Engineering, Amazon Rekognition and Video