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Democratizing AI Compute, Part 9: Why Do HW Companies Struggle to Build AI Software?

April 22, 2025

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Chris Lattner

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Democratizing AI Compute, Part 8: What about the MLIR compiler infrastructure?

April 8, 2025

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Chris Lattner

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Democratizing AI Compute, Part 7: What about Triton and Python eDSLs?

In this post, we’ll break down how Python eDSLs work, their strengths and weaknesses, and take a close look at Triton.

March 26, 2025

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Chris Lattner

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Democratizing AI Compute, Part 6: What about AI compilers (TVM and XLA)?

March 12, 2025

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Chris Lattner

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Democratizing AI Compute, Part 5: What about CUDA C++ alternatives like OpenCL?

March 5, 2025

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Chris Lattner

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Democratizing AI Compute, Part 4: CUDA is the incumbent, but is it any good?

Answering the question of whether CUDA is “good” is much trickier than it sounds.

February 20, 2025

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Chris Lattner

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Democratizing AI Compute, Part 3: How did CUDA succeed?

If we as an ecosystem hope to make progress, we need to understand how the CUDA software empire became so dominant.

February 12, 2025

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Chris Lattner

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Democratizing AI Compute, Part 2: What exactly is “CUDA”?

February 5, 2025

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Chris Lattner

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Democratizing AI Compute, Part 1: DeepSeek’s Impact on AI

Part 1 of an article that explores the future of hardware acceleration for AI beyond CUDA, framed in the context of the release of DeepSeek

January 30, 2025

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Chris Lattner

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AI Regulation: step with care, and great tact

AI systems take an incredible amount of time to build and get right - I know because I have helped scale some of the largest AI systems in the world, which have directly and indirectly impacted billions of people. If I step back and reflect briefly - we were promised mass production self-driving cars 10+ years ago, and yet we still barely have any autonomous vehicles on the road today.

September 26, 2023

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Tim Davis

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