August 29, 2022

The Amazon EC2 Instances You Didn’t Know You Needed

Any AWS user knows how important Amazon EC2 instances are. In fact, EC2 is the most used AWS service by a huge margin. Here at Cloudvisor, 45% of spend every month is spent on EC2 services. However, you may not be using EC2 to its fullest extent. There are many services that most users don’t know about, and you might be missing out on some real benefits that could help cut significant costs for your startup.

Let’s dive into the wonderful world of EC2 and how you can use these overlooked compute types to give your AWS implementation the performance boost it needs.

Develop iOS Apps with Mac instances

Despite iOS apps accounting for 63% of total app revenue in 2021, many users are unaware that they can natively develop Mac and iOS applications using EC2 Mac instances. This option is great for anybody who wants to quickly provision a macOS testing environment or ensure that their products work well with Apple products. If you want to develop anything for Mac, EC2 Mac instances are a no-brainer. However, like everything in AWS, there’s a lot of choice on offer.

Amazon EC2 Mac instances are based on Apple Mac Mini computers, with two architecture options available. You can opt for x86-based EC2 Mac instances, which feature Intel Core i7 processors and are powered by the AWS Nitro System, but there’s a better, newer option.

The EC2 M1 instance takes advantage of Apple’s incredible M1 chipset to deliver 60% better price-performance over x86-based EC2 Mac instances for iOS and macOS application build workloads. In addition, M1 instances also enable Arm64 macOS environments for the first time on AWS. They additionally support macOS Big Sur (version 11) and macOS Monterey (version 12) as Amazon Machine Images (AMIs).

It’s one hell of a leap, and we’d always opt for EC2 M1 instances for Mac workloads, given such a choice. Whichever instance you choose, it is possible to rent an instance for 24 hours and develop Mac and iOS applications natively or build workflows from your local iOS & macOS applications to one based in the cloud.

GPU EC2 instances for all your mobile game development and machine learning needs

Graphics cards aren’t just for crypto-miners and gamers. While they are essential for game developers, they also empower software engineers to create complicated machine learning solutions. AWS offers a number of GPU-based EC2 instances that can be tailored for your specific workloads.

EC2 G5 & G5g: Perfect For Game Developers

If you’re a mobile game developer, then you will want to check out Amazon EC2 G5 & G5g instances. These instances offer the best price-performance for EC2-powered android game streaming. They come in with AMD or AWS Graviton ARM-based CPUs, together with Nvidia TG4 or A10G Tensor Core GPUs.

Its primary purpose is for remote game streaming, as G5g is the first ARM-based instance in a major cloud provider to feature GPU acceleration. This enables you to render Android games natively and then stream over the cloud directly to the end-users devices.

With that said, G5 & G5g also have a broad application for machine learning and deep learning solutions and are a cost-effective option to deploy these kinds of applications. However, there is another instance type that is tailor-made for machine learning training.

EC2 P4d: Perfect for machine learning training

EC2 p4d is the best choice if you want to train your (medium to complex) machine learning program. It runs using Nvidia A100 Tensor Core GPUs, with an impressive 400 Gbps of networking throughput and low latency. Each instance can have up to 8 Nvidia A100 Tesnor Core GPUs, and when grouped into EC2 UltraClusters, a P4d setup can rival purpose-built supercomputers.

This enables you to run highly sophisticated multi-node machine learning training and distributed HPC workloads. It is possible to scale from a handful of GPUs to thousands based on specific project needs. This makes EC2 p4d a great fit for machine learning developers.

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Accelerated instances for almost any purpose

GPU instances can be used for most purposes, but they are more like a jack-of-all-trade workhorse. In general, you will get more bang for your buck if you look at solutions that use highly specialized processors, known as accelerators, that are specifically built to excel at a single task. If you know exactly what you need your AWS implementation to do, Accelerated EC2 instances are for you.

EC2 DL1: A great tool for Deep Learning models

Unlike our previous two instances, EC2 DL1 is powered by Gaudi accelerators from Habana Labs, an Intel company. Its primary purpose is to train deep learning models for natural language processing, object detection, and image recognition.

This highly specialized solution offers up to 8 Gaudi accelerators per instance, with 4TB of local NVMe storage and 400 Gbps of networking throughput. These highly specialized processors offer a 40% better price-performance ratio compared to GPU-based instances when training deep learning models.

EC2 F1: The Best Way To Process Complicated Datasets

Are you working on solving the big problems? Then EC2 F1 is the solution for you. This instance takes advantage of the flexibility offered by Field Programmable Gate Arrays (FPGAs) to solve complex science, engineering, or business problems.

These FPGAs can be tailored to target workloads in genomics, search/analytics, image and video processing, network security, electronic design automation, file compression, and big data analytics. The best part? You don’t need to maintain the very costly, highly specialized infrastructure that comes with creating your own FPGA systems in-house.

EC2 VT1: A solution for real-time 4K UHD video transcoding 

Video transcoding is becoming an increasingly large cost burden for video and event broadcasters, but EC2 VT1 offers a low-cost way to deliver real-time video transcoding, with support for 4K UHD resolution and up to 64 simultaneous 1080p60 streams per instance.

This tailored instance is able to deliver a 30% lower cost per stream compared to Amazon EC2 G4dn GPU-based instances and 60% lower cost per stream compared to EC2 C5 CPU-based instances for transcoding live video streams.

EC2 trn1: The ultimate machine learning training solution

EC2 trn1 is based on AWS’s in-house chip Trainium. This solution is designed to be the best-in-class accelerator for training deep learning models for natural language processing, computer vision, search, recommendation, ranking, and much more.

Each Trn1 instance supports up to 16 AWS Trainium accelerators, up to 800 Gbps of Elastic Fabric Adaptor networking bandwidth, and 768 GB/s of NeuronLink connectivity. It is useful for a variety of applications, including seismic analysis, financial modeling, or financial discovery.

Trn1 instances are deployed in Amazon EC2 UltraClusters, which consist of tens of thousands of Trainium accelerators, enabling it to rapidly train even the most complicated Deep Learning models with millions of parameters.

EC2 Inf1 – The most affordable way to develop and run machine learning applications

EC2 Inf1 was created to enable customers to run large-scale machine learning inference applications, such as search, recommendation engines, computer vision, speech recognition, natural language processing, personalization, and fraud detection, at an unbeatable price point.

These instances are able to provide high-performance machine learning inference at a 70% lower cost per inference than comparable GPU-based EC2 instances. They do this while simultaneously delivering a 2.3 times higher throughput than other solutions.

Amazon Bracket provides a fully managed quantum computing service

It is difficult to grasp just how much faster quantum computers are compared to regular computers (in certain workloads). Normal computers use “bits”, which can either be a one or a zero. In contrast, quantum computers use Qubits, which can simultaneously be 1 and 0. This means that a quantum computer doesn’t need to wait for one process before another can begin, but it can instead process everything simultaneously.

This is incredibly useful for scientists. Quantum computers can solve complex simulations 158 million times faster than modern supercomputers, but there’s one drawback: Cost.

While you can get an affordable SpinQ quantum computer, with 2 qubits of processing power, for just $5,000, a true commercial system would cost you between $10 and $15 million, not to mention all the knowledge and maintenance these cutting-edge devices require.

Fortunately, Amazon Bracket offers a way for scientists to get access to the power of quantum computing through a fully managed service. It is designed to research quantum computing algorithms, test quantum hardware, and build quantum software.

Instead of building all that infrastructure, you simply rent a quantum computer for as long as you need it. This provides researchers with unparalleled flexibility and gives them the ability to tackle problems without massive research grants.

Need Help Determining The Best EC2 Instance For Your Needs?

We’ve provided a glimpse into the possibilities of specialized EC2 instances, and deciding on the best one for your use case can be challenging. That’s where Cloudvisor comes in. Our team will work with you to help determine the best EC2 instance for your use case, and a plan for how to most efficiently implement it. We can even help you determine if your startup is eligible for AWS credits.

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