Machine learning development requires a lot of computing power. Specifically GPU powered computing. GPUs are super expensive on incumbents like AWS. This creates a big divide between compute rich and compute poor developers and teams. Thus becoming a bottleneck for over 5 Million ML devs.
At Q Blocks, we have figured out a new way to solve this. To bring access to the most powerful GPUs at 1/10th the cost with the reliability and scalability of a cloud.
Try Q Blocks and save significant costs for training and tuning your next ML model
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Based on our record, Microsoft Azure seems to be a lot more popular than QBlocks Cloud. While we know about 65 links to Microsoft Azure, we've tracked only 1 mention of QBlocks Cloud. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
There's a middle way: qblocks.cloud - This platform that enables access of unused GPU servers across the globe. It works like a traditional cloud by offering scalability, security and reliability while being upto 80% low cost for AI workloads. Also, offers inbuilt Jupyterlab, AI framework, GPU driver support out of the box. Thus no time wasted in server setup. Source: over 1 year ago
The first step in creating a virtual machine is getting a Microsoft account. Once you have a Microsoft account click this link to create an Azure free trial account. Click on the "Try Azure for free" button. This takes you to the page below. - Source: dev.to / about 1 month ago
Before you start, ensure you have an active Azure subscription, if you don't have one, Click here to create a free account. - Source: dev.to / 2 months ago
A VM is the original “hosting” product of the cloud era. Over the last 20 years, VM providers have come and gone, as have enterprise virtualization solutions such as VMware. Today you can do this somewhere like OVHcloud, Hetzner or DigitalOcean, which took over the “server” market from the early 2000’s. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft's Azure also offer VMs, at a less... - Source: dev.to / 4 months ago
Before deploying the application with Kubernetes, you need to containerize the application using docker. This article shows how to deploy a Flask application on Ubuntu 22.04 using Minikube; a Kubernetes tool for local deployment for testing and free offering. Alternatively, you can deploy your container apps using Cloud providers such as GCP(Google Cloud), Azure(Microsoft) or AWS(Amazon). - Source: dev.to / 4 months ago
Consider cloud storage services for offsite storage and automation (Azure, AWS, GCP). - Source: dev.to / 9 months ago
Amazon AWS - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.
Google Cloud Platform - Google Cloud provides flexible infrastructure, end-to-security, modern productivity, and intelligent insights engineered to help your business thrive.
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
Frontegg - Elegant user management, tailor-made for B2B SaaS
Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.Sign up to Linode through SaaSHub and get a $100 in credit!
Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.