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WeAreDevelopers LIVE • Feb 5, 2025

CUDA in Python

Andy Terrel

Tired of choosing between Python productivity and C++ hardware speed? Discover how new CUDA tools finally give developers low-level GPU control for maximum performance directly in Python.

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#1 about 3 min

Understanding the CUDA abstraction stack for developers

Different abstraction layers from PyTorch down to PTX expose unique GPU capabilities.

#2 about 3 min

Bridging the productivity and speed gap in Python

CUDA Python eliminates the rigid tradeoff between high-level scripting productivity and low-level hardware performance.

#3 about 4 min

Optimizing performance through kernel fusion with nvMATH

Fusing matrix multiplication and epilogues into a single kernel prevents excessive host-to-device communication overhead.

#4 about 4 min

Enabling device-side function calls with Numba JIT

Just-in-time linking with LLVM grants Python kernels direct internal access to compiled C++ device functions.

#5 about 3 min

Controlling parallelism and block reductions in Python

CUDA Cooperative enables granular control over threaded hardware blocks without exiting the Python execution environment.

#6 about 3 min

Exposing low-level hardware features via Numba CUDA

Translating C++ templates allows Python developers immediate implementation of optimized hardware floating point types.

#7 about 3 min

Managing host-side GPU resources using Python objects

A native object model replaces complex bindings for instantiating streams, events, and programmatic compiler representations.

#8 about 1 min

Accessing educational resources for Python accelerator computing

Forking the GitHub repository and evaluating reference examples immediately builds proficiency in accelerated Python development.

Matching moments

1:11 min

The expanded CUDA ecosystem and native Python support

Paul Graham Paul Graham · World Congress 2026 Europe

4:37 min

Simplifying parallel programming with the CUDA ecosystem

Paul Graham Paul Graham · LIVE

1:51 min

Accelerating math operations and kernel fusion with nvmath-python

Paul Graham Paul Graham · World Congress 2025

2:27 min

Exploring the CUDA ecosystem and levels of abstraction

Paul Graham Paul Graham · World Congress 2025

1:49 min

Accelerating script execution with CuPy and Numba kernels

Paul Graham Paul Graham · World Congress 2026 Europe

1:37 min

Accelerating compute with focused developer tools

Julia Koch Julia Koch +1 · World Congress 2026 Europe

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Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 25, 2026 · 11:00–11:30

Stage 5

Managing GPUs by Just Asking, Infrastructure in the Age of MCP

Jessica Garson Beauchemin

Developer Relations Lead, Community at Runpod

Jessica Garson Beauchemin
Open session

World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 6

Compute for your AI model: GPUs, LPUs, TPUs and beyond..

Kushaagra Goyal

Tech Lead at Rubrik, ex-CTO at Gan.AI, ex-Databricks

Kushaagra Goyal
Open session

World Congress 2026 North America

September 25, 2026 · 15:30–16:00

Stage 7

Trust, But Verify: Continuous GPU Validation at Scale

Kyle Bell

VP of AI at TensorWave

Kyle Bell
Open session

World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 1

Anatomy of an AI Request: Where Latency and Cost Are Really Born

Dan Fu

VP of Kernels at Together AI

Dan Fu
Open session

World Congress 2026 North America

September 23, 2026 · 10:45–12:45

Stage 10

Agents That Own Their Inference: Building Production AI Agents on Dedicated GPUs

Khaja Omer, Sheilah Kirui

Khaja Omer
Sheilah Kirui
Open session

World Congress 2026 North America

September 24, 2026 · 15:30–16:00

Stage 7

Beyond Vibe Coding: Using CLI Tools as Your AI Counterpart in Pair Programming

Ron Veen

Java enthusiast and Special agent for Team Rockstars IT

Ron Veen

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