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Learning a new programming language is hard. You have to learn new syntax, keywords, and best practices, all of which can be frustrating when you’re just starting. In this blog post, I want to share a gentle introduction to Mojo from a Python programmer’s perspective.

‍On May 2nd, we announced our next-generation AI developer platform with two exciting breakthrough technologies — the Mojo programming language and the Modular AI Engine. In just over two months, more than 110k developers have signed up for the Mojo Playground to learn Mojo and experience its performance firsthand, over 30k developers have signed up to our waitlist for the AI engine, and our Modular community on Discord has grown to 17k developers! We’re incredibly excited to see developers sharing their experience with Mojo, providing product feedback, and learning from each other.

Today’s AI infrastructure is difficult to evaluate - so many converge on simple and quantifiable metrics like QPS, Latency and Throughput. This is one reason why today’s AI industry is rife with bespoke tools that provide high performance on benchmarks but have significant usability challenges in real-world AI deployment scenarios.

We’re very excited about the positive reception of Mojo since its launch as well as the community of people building around it. Given new Large Language Model (LLM) powered developer tools like Copilot and Ghostwriter, many developers are wondering about the future of programming – do programming languages still matter when AI writes the code?

A few weeks ago, we announced the world’s fastest unified AI inference engine. The Modular AI Engine provides significant usability, portability, and performance gains for the leading AI frameworks — PyTorch and TensorFlow — and delivers world-leading execution performance for all cloud-available CPU architectures.

Last week, we launched Modular to the world after more than 16 months in stealth. We started Modular with a deep conviction — after 6+ years of building and scaling AI infrastructure to billions of users and 20+ years of building foundational compute infrastructure — it was clear the world needed a better path forward. Everyone wants less complexity, better access to compute and hardware, and the ability to develop and deploy AI faster.

We’re excited to finally share what we’ve been building at Modular. This announcement begins Modular’s journey to radically change the nature of AI programmability, usability, scalability, and compute.

In this post, we describe Modular’s approach to solving this problem and its game-changing benefits, including a new standard in state-of-the-art (SOTA) performance on CPU as compared to existing solutions.

AI is powered by a virtuous circle of data, algorithms (“models”), and compute. Growth in one pushes needs in the others and can grossly affect the developer experience on aspects like usability and performance. Today, we have more data and more AI model research than ever before, but compute isn’t scaling at the same speed due to … well, physics.

At Modular, we are rebuilding AI infrastructure for the world. Our goal is to move past AI tools that are themselves research projects and into a future where AI development and deployment are orders of magnitude more efficient for everyone. You should be able to do this without trading off performance or having to rewrite your entire code base.