标签：#ec2

This article provides a step by step deployment guide for serving Google's Gemma 4 on an AWS EC2 G5g instance using pure JAX. The code is here: github.com/xbill9/gemma4-dev What is this project trying to Do? This project aims to serve a modern open model on the cheapest whole CUDA GPU AWS will rent you, and to measure honestly what that costs. Aren't You Using The Wrong GPU? Probably! The T4G is a Turing chip from 2018. It has no bfloat16 and no fp8. But it is cheap, it is available when nothing else is, and it is attached to a Graviton2 host — which makes G5g the rare hardware axis that almost nothing in the ML ecosystem targets: aarch64 and CUDA together . So let's give pure JAX a shot on G5g! AWS EC2 G5g G5g instances pair an AWS Graviton2 (64-bit Arm) processor with NVIDIA T4G Tensor Core GPUs. At g5g.xlarge they are the cheapest EC2 instance carrying a whole NVIDIA GPU , and the only Arm-based GPU family AWS offers. Two GPU instances are cheaper per hour and neither can serve this model (us-east-1, Linux, on-demand, checked against the Pricing API on 2026-08-28): g6f.large at $0.2020 is genuinely NVIDIA and genuinely CUDA — but it is one eighth of a GPU with 3 GB , and the weights alone are 6.155 GB. The first g6f that fits is g6f.4xlarge at $0.9500, which is 1.7x this rig's g5g.2xlarge . g4ad.xlarge at $0.3785 carries an AMD Radeon Pro V520 — no CUDA at any price. Among whole NVIDIA GPUs, G5g is the floor: g5g.xlarge at $0.4200, and the next one up is g4dn.xlarge at $0.5260. More information is available here: https://aws.amazon.com/ec2/instance-types/g5g/ The default in this rig is g5g.2xlarge — 1 GPU, 8 vCPU, 16 GiB RAM. Note- the T4G reports 15,360 MiB of device memory, not the nominal 16 GB. Budget against the measured number. Gemma 4 Gemma is Google's family of open models built from the same research as Gemini. This rig serves google/gemma-4-E2B-it , the instruction-tuned reference release. JAX JAX is Google's array computing library — NumPy semantics, c

Today I did something I've wanted to do for a while — I took an app running on my own laptop and put it "live" on the internet using AWS. It sounds scary when you read about it online, but once I actually did it, it was just a bunch of small, simple steps, one after another. This post is me writing down everything I did, in plain, easy words, so that if you're a beginner like me, you can follow along without getting confused by fancy tech terms. What is AWS, in simple words? AWS (Amazon Web Services) is basically Amazon renting out computers over the internet. Instead of buying your own physical server and keeping it running 24/7 at home, you "rent" a computer from Amazon. That computer runs your app, and anyone with the internet can visit it. The specific service I used is called EC2 . Think of EC2 as one virtual computer that lives in Amazon's data center, and you get to control it like it's your own. Step 1: Set up IAM first Before touching any servers, I went to IAM (Identity and Access Management). This is AWS's way of managing "who is allowed to do what" in your account. In simple words: instead of using your main AWS login for everything (which is risky), IAM lets you create a separate user with its own permissions. It's like giving someone a spare key instead of your master key. I set this up first so my account stays safer. Step 2: Launch an EC2 instance Next, I went to the EC2 section and launched a new instance (a fancy word for "a virtual computer"). During this step, AWS also lets you create a .pem file — this is basically a secret key file. It's like a digital key to a lock. Only someone with this file can get into the server. I downloaded it and kept it safe, because if you lose it, you can't easily get back in. Step 3: Login to the server using SSH Once the server (EC2 instance) was ready, I needed a way to "log in" to it from my own laptop. For that, I used something called SSH, along with the .pem key file I downloaded earlier. In simple words: SSH

Building a Spring Boot application is only half the journey. At some point, every backend project reaches the next question: Where should this application actually run? If you are deploying on AWS, two common options are: Amazon EC2 Amazon ECS Both can run the same Dockerized Spring Boot application. But they are not the same kind of deployment model. EC2 gives you a server. ECS gives you container orchestration. Choosing between them is less about which one is “better” and more about which one fits the stage of your application. Starting with EC2 EC2 is the most direct way to run a Spring Boot application on AWS. You create a virtual machine, install what you need, and run your application. For a Dockerized Spring Boot app, the flow often looks like this: GitHub Actions ↓ SSH into EC2 ↓ Pull latest code or image ↓ Build / run Docker container ↓ Spring Boot app runs on EC2 The mental model is simple: One server One Docker container One Spring Boot application That simplicity is useful. Especially when you are launching an MVP, a freelancer project, an internal tool, or your first production version. Why EC2 Works Well for Many Spring Boot Apps EC2 is not outdated just because newer deployment platforms exist. For many applications, EC2 is enough. It works well when: traffic is predictable the application is small or medium-sized you want full control over the server you want simple debugging through SSH you do not need multiple replicas yet you want to keep infrastructure easy to understand A common setup might be: Spring Boot Dockerfile Docker Compose GitHub Actions EC2 That setup can be production-ready for many early-stage apps. It gives you: a real deployment target repeatable Docker builds automated deployment through GitHub Actions simple logs direct server access For a solo developer or small team, that matters. You can understand the entire deployment flow without needing a full container orchestration platform. The Tradeoff With EC2 The tradeoff is that you

Introduction In my previous IAM article we learnt basics of IAM and how to create Users, Groups and attach Policies. You can refer here: https://dev.to/kadhamvj23/aws-identity-and-access-management-explained-for-beginners-cn7 After setting up secure access to our AWS account using IAM, the next question we mostly have is where do we actually run our application? The answer is Amazon EC2 - Elastic Cloud Compute. EC2 is one of the most widely used AWS services and understanding it well is essential for anyone starting their cloud journey. In this article we will cover what EC2 is, why it exists, the different types of instances, pricing models, Regions and availability Zones and finally hands-on walk through of creating your first EC2 instance. Breaking Down the Name -EC2 Let us understand what each word in the name actually means: Elastic --> In AWS you will notice many services have this prefix "Elastic". The reason is simple. Whenever AWS provides a service that can be scaled up or scaled down based on our needs, that service is called Elastic . With EC2 you can increase resources when traffic is high and decrease them when the traffic is low. So in simple terms EC2 = A virtual server on the cloud that you can resize anytime. Cloud: EC2 runs on AWS's public cloud infrastructure, meaning the servers are owned and managed by Amazon across the world. Compute: The word compute means you are asking AWS to provide you CPU, RAM and Disk - basically a virtual machine or server that can run your applications. How does EC2 actually work? When you request a Virtual server from AWS, here is what happens behind the scenes: You request a virtual machine on AWS ⬇️ request goes to a Hypervisor(a software layer sitting on top of physical servers that creates and manages VMs) ⬇️ Hypervisor creates your VM ⬇️ You get the access to your EC2 instance You never touch any physical hardware. AWS manages all of that for you. Why use EC2? Imagine your company wants to host an application. T