Have you attempted to rent a GPU recently? It is difficult, right? They get booked all the time! The AI boom has made GPUs the hottest commodity in tech right now. Cloud providers are working overtime to accommodate them as developers and companies everywhere require GPUs to train and run AI models.
You may be asking yourself what the issues are? The issue is, everyone needs it all at once. Large tech companies such as OpenAI, Meta, and Google consume huge amounts of GPUs simply to train their employees. Even cloud providers such as AWS, Google Cloud, and Microsoft Azure are running out of inventory. Have you attempted to launch a GPU server only to get the "no capacity available" error? That's the issue.
For that reason, many more people are choosing to rent a GPU rather than buy one. Renting makes things easier for you because you:
• Only pay for what you use, rather than buying hardware.
• Can quickly scale up the power when you need it.
• Avoid all of the hardware issues (cooling, maintenance, etc.).
Still, the rental prices keep skyrocketing. Some developers get around this by using shared GPU servers, or spot instances (temporary servers), but these can shutdown during usage—really annoying when you are training a model!
As it stands, GPUs are the bedrock of AI advances; whomever owns more GPUs will build and train models faster than those who do not.
Still, it is fun to watch, isn't it? AI runs on GPUs and GPUs are running the world.
Why the Sudden Demand for GPUs, All At Once?
AI tools generally require a lot of power because the computation needed to support these applications is immense. Therein lies the benefit of having a GPU (Graphics Processing Unit). Regularly, a CPU can only complete one task at a time whereas a GPU can tackle thousands of tasks concurrently. That's why they are suited for training huge AI models, including chatbots and image generators.You may be asking yourself what the issues are? The issue is, everyone needs it all at once. Large tech companies such as OpenAI, Meta, and Google consume huge amounts of GPUs simply to train their employees. Even cloud providers such as AWS, Google Cloud, and Microsoft Azure are running out of inventory. Have you attempted to launch a GPU server only to get the "no capacity available" error? That's the issue.
Why Are GPU Rentals So Pricey?
The short answer is: there are many individuals who want GPUs and not enough to go around. The latest and most powerful GPUs are expensive and not widely accessible—NVIDIA H100, and A100 are too good for this world. It takes months and millions to build out an entire data center with these chips.For that reason, many more people are choosing to rent a GPU rather than buy one. Renting makes things easier for you because you:
• Only pay for what you use, rather than buying hardware.
• Can quickly scale up the power when you need it.
• Avoid all of the hardware issues (cooling, maintenance, etc.).
Still, the rental prices keep skyrocketing. Some developers get around this by using shared GPU servers, or spot instances (temporary servers), but these can shutdown during usage—really annoying when you are training a model!
What Lies Ahead for Cloud Providers?
Cloud companies are clamoring to develop more data centers and acquire additional GPUs. Amazon is implementing additional GPU zones and new small companies like CoreWeave and Lambda Labs are solely focused on AI workloads. These smaller companies are providing users with faster, less expensive access to GPUs.As it stands, GPUs are the bedrock of AI advances; whomever owns more GPUs will build and train models faster than those who do not.
Final Thoughts
GPUs are the new "gold" in tech—everyone wants them and there are not enough to go around. If you are working on AI, you should prepare yourself for higher prices and waiting longer to obtain them.Still, it is fun to watch, isn't it? AI runs on GPUs and GPUs are running the world.
