Data Exchange Interview
Interview with Tim Davis, Co-Founder of Modular. Full interview here
Ben (Host): Welcome to the Data Exchange Podcast. Today we’re joined by Tim Davis, co-founder and Chief Product Officer at Modular. Their tagline says it all: The future of AI development starts here. Tim, great to have you on the show.
Tim Davis: Great to be here, Ben—thanks for having me.
Introducing Mojo: Python, Reimagined
Ben: Let’s dive right in. What is Mojo, and what can developers use today?
Tim: Mojo is a new programming language—a superset of Python, or “Python++,” if you will. Right now, anyone can sign up at modular.com/mojo to access our cloud-hosted notebook environment, play with the language, and run unmodified Python code alongside Mojo’s advanced features.
“All your Python code will execute out of the box—you can then take performance-critical parts and rewrite them in Mojo to unlock 5–10× speedups.”
That uplift comes from our state-of-the-art compiler and runtime stack, built on MLIR and LLVM foundations.
Solving the Two-Language Problem
Many ML frameworks hide C++/CUDA complexity behind Python APIs, but that split still causes friction. Mojo bridges the gap:
Prototype in Python
Optimize in Mojo (same codebase)
“Researchers no longer need to drop into C++ for speed; they stay in one language from research to production.”
This unified model dramatically accelerates the path from idea to deployment.
Who is Mojo For?
Ben: Frameworks like TensorFlow and PyTorch already tackle performance. Who’s Mojo’s target audience?
Tim: Initially, it’s us—Modular’s own infrastructure team. But our real audience spans:
Systems-level ML engineers who need granular control and performance.
GPU researchers wanting a seamless path to production without rewriting code.
By meeting developers where they are, Mojo helps defragment fragmented ML stacks and simplifies pipelines.
Under the Hood: Hardware-Agnostic Design
Mojo’s architecture is built for broad hardware support:
MLIR (Multi-Level IR): Provides a common representation across hardware.
LLVM Optimizations: Powers high-performance codegen.
Multi-Hardware Portability: CPUs, GPUs, TPUs, edge devices, and beyond.
“We want access to all hardware types. Today’s programming model is constrained—Mojo opens up choice.”
This means you’re not locked into CUDA or any single accelerator vendor.
Beyond the Language: Unified AI Inference Engine
Modular also offers a drop-in inference engine:
Integrates with Triton, TF-Serving, TorchServe
CPUs first (batch workloads), GPUs coming soon
Orders-of-magnitude performance gains
“Simply swap your backend and get massive efficiency improvements—no changes to your serving layer.”
Enterprises benefit from predictable scaling and hardware flexibility, whether on Intel, AMD, ARM-based servers, or custom ASICs.
Roadmap: Community, Open Source & Enterprise
Next 6–12 Months:
Expand Mojo’s language features (classes, ownership, lifetimes).
Enable GPU execution (beyond the cloud playground).
Extend the inference engine to training, dynamic workloads, and full pipeline optimizations (pre-/post-processing).
“We released early to learn from real users—80,000 sign-ups across 230+ countries. Their feedback drives our roadmap.”
Why a New Language Matters
Mojo’s core value prop can be summed up in three words:
Usable: Drop-in Python compatibility; gentle learning curve.
Performant: Advanced compiler + runtime yields 5–10× speedups out of the box.
Portable: Write once, run anywhere—from cloud GPUs to mobile CPUs.
Together, these unlock faster innovation, lower costs, and broader hardware choice.
Democratizing AI Development
In Tim’s own words:
“Our mission is to make AI development accessible to anyone, anywhere. By rethinking the entire stack, we’re unlocking a new wave of innovation and putting compute power in more hands.”
With its unified language and inference engine, Modular is ushering in a future where AI development truly starts here—for researchers, engineers, and enterprises alike.
