Artificial Intelligence (AI) has been a hot topic in recent years, with many experts hailing it as a revolutionary technology that has the potential to greatly impact how individuals live and work. However, despite some notable achievements, the real-world impact of AI has been relatively limited.
AI has been utilized in various applications, such as self-driving cars, facial recognition systems, and voice assistants. It enables computers to undertake tasks that once required human intelligence, like playing chess or identifying objects in photographs. However, according to Banana. dev founding engineer Sahil Chaudhary, AI has yet to have a big and lasting effect on most people’s lives.
Pricey and sluggish
One major obstacle that has hindered the widespread adoption of AI is its complexity and high cost. Developing and implementing AI algorithms can be prohibitively expensive for many businesses, with the average cost of implementation being around $300,000 and taking between 6 and 12 months to fully deploy. Additionally, studies have shown that businesses that use AI-based systems often have slower processing times than those that do not.
Sahil’s solution
While most people in the AI industry concentrate on creating new and improved models, Sahil focuses on ensuring that these models run efficiently in applications so that they can have a real-world impact.
To address these issues, Sahil and the team at Banana. dev have developed serverless GPUs, which removes the need of running expensive GPUs always on and only running them when needed, making this significantly less expensive and more energy efficient than traditional methods.
Enabling serverless GPUs comes with a fundamental problem which is that machine-learning models are very slow to start which means if you don’t keep them running always on, your users will have to wait multiple minutes when the model is started. TO solve this problem, Sahil and his team at Banana. dev also introduced GPU TurboBoot, a way for large machine-learning models to start up in one second. TurboBoot uses a combination of techniques, including proprietary algorithms, to make AI faster and more efficient.
“AI models take a long time to “warm up” before they are ready to be used, which meant you had to keep them running constantly, paying for expensive servers, but with this project, we reduced this warm-up time to 1-2 seconds, which means you can now run servers only when needed instead of constantly,” he explains.
A positive step
In conclusion, Serverless GPUs powered by TurboBoot are a positive step towards making AI faster and more affordable for real-world applications. Reducing the time and resources needed for AI development and implementation can help businesses stay competitive and take advantage of AI’s benefits. By using GPU technology, the system can also improve the speed and efficiency of AI-driven applications, making them more useful and effective.
Overall, Serverless GPUs are a promising solution that could help overcome some obstacles associated with making AI more accessible and effective for practical applications.
