Hi folks!
I’m currently conducting experiments for an academic paper and am in need of access to high-end GPUs like A100/H100, along with significant storage (200-400GB) for large datasets.
Unfortunately, Lambda Labs isn’t available in my location here in India. I’m actively looking for alternative cloud GPU services that are cost-effective and reliable for this research.
Any suggestions or experiences with providers that fit these criteria would be greatly appreciated!
Thanks so much for any help you can offer! 
Heyya @sakshikuchroo!!! 
That’s a common challenge when you need specific hardware for research, especially in different regions! Finding good cloud GPU services in India as an alternative to Lambda Labs is definitely something others here might have tackled.
“Google Cloud Platform (GCP)”
Google Cloud offers access to top-tier GPUs such as the A100 and even the V100. You can easily set up a VM with GPU resources and manage large storage needs using Google Cloud Storage. The cost can vary based on the resources you choose, but there are discounts and billing options like preemptible VMs that can help reduce costs significantly.
I’ve used GCP for large-scale deep learning projects and found that they offer flexible options for storage. The A100 GPU instances are powerful and cater well to deep learning workloads. Additionally, you can leverage LambdaTest if you need cross-browser testing of web-based interfaces during your experiments, as it allows testing across multiple environments without worrying about GPU limitations.
Hope this provides a solid option for your academic work! Best of luck!
Answering your question @sakshikuchroo by building on the suggestion from @Joe-Elmoufak regarding GCP, I wanted to add another major cloud provider that might fit your needs, especially concerning cost-effectiveness.
I agree that exploring different providers is key when Lambda Labs isn’t available. Amazon Web Services (AWS) offers a variety of GPU-backed instances, including A100s and other powerful GPUs, like the P4 and P3 series. If you’re on a budget, you can use spot instances, which can provide substantial savings while still offering good performance. You can also use S3 for storage, which is scalable for large datasets.
If you want to refer to my experience, I have used AWS EC2 with A100 GPUs for a few months. By leveraging spot instances, I was able to conduct my experiments at a fraction of the cost. AWS also provides detailed billing and monitoring tools, so you can track your usage and make sure you’re staying within budget. It’s a great choice for scalability and flexibility.
Hope this gives you another strong alternative to consider!
Thank You.
Well… Hello there!! Joining this helpful discussion initiated by @sakshikuchroo and continued by @Joe-Elmoufak and @dimplesaini.230 about finding cloud GPUs in India for academic research! It’s great to see GCP and AWS being suggested.
I wanted to throw another potential option into the ring that you might find particularly cost-effective. Paperspace offers a variety of GPU instances, including the A100 and other powerful GPUs suitable for AI research. Their pricing is often more affordable compared to some of the larger cloud providers, and they offer excellent support for machine learning workloads.
Storage options like Block Storage are also available for large datasets.
Based on my experience, I’ve been using Paperspace for smaller machine learning projects and found it to be very efficient and cost-effective. The interface is easy to use, and they offer pay-as-you-go pricing.
I also found their API integration for machine learning workloads useful, which saved a lot of time in automating experiments. If you also need to run tests on web-based applications alongside your GPU workloads, LambdaTest is another useful tool for ensuring your web interfaces work properly across different browsers.
Happy researching! 