Tianqi Chen

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@tqchenml

AssocProf @CarnegieMellon. Distinguished Eng @NVIDIA. Creator of @XGBoostProject, @ApacheTVM. Member catalyst.cs.cmu.edu, @TheASF. Views are on my own

CMU tqchen.com Joined May 2015

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Tianqi Chen @tqchenml Nov 4

In the age of agents, do we still need compilers at all, and if so, how do we design ML compilers for agents? Checkout take and effort on a ground up rebuild of a ML compiler foundation for the Agentic Era

Bohan Hou Verified account @bohanhou1998 Nov 4

After our kernel-agent experiments, we had to confront an uncomfortable question: Do we still need DSLs and ML compilers when agents can do it all？

In short: We're still here! 😎

If you're curious why, here's our take:
tvm.apache.org/2026/11/03/ret…

We've also rebuilt TIRx from the ground up as a shared DSL and compiler foundation for agentic kernel development.

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Tianqi Chen @tqchenml Oct 5

KCoral scales up agentic benchmarking for kernel agents, check it out

Yixin Dong Verified account @yi_xin_dong Oct 5

Kernel agents can already beat hand-tuned kernels. Spinning up more agents is easy, but scaling GPU evaluation is still hard and expensive.

What happens when the agents scale faster than the GPUs?

Meet KCoral 🪸: a lightweight shared GPU benchmark environment for agentic kernel development.

🧑‍💻 Agents keep writing and revising code in their own workspace.
⚡ Evaluation is sent to KCoral, which schedules a shared GPU pool and returns correctness and timing.
🌐 Scale across multiple GPUs and nodes through a unified API endpoint.
🔒 Per-request isolation with only milliseconds of execution overhead.

By overlapping CPU compilation with GPU work across requests, KCoral further speeds up kernel evaluation. On a single NVIDIA B200, this translates to 2.58× the throughput of sequential local execution.

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Tianqi Chen @tqchenml Sep 30

We will be teaching Agentic GPU Programming for MLSys as part of the CMU ML Systems course this upcoming spring at @SCSatCMU, touching on knowledge curation, compiler analysis, and feedback loops. We also curated an online book on the topic, check it out mlc.ai/agentic-gpu-pr…

Tianqi Chen @tqchenml Sep 29

In the age of agentic programming, the compiler's role will increasingly move from just supporting compilation to a flexible harness that agents interact with and rely upon. Compiler harness will be the new frontier in agentic GPU programming.

Hongyi Jin Verified account @HongyiJin258 Sep 29

AI agents are becoming GPU kernel engineers. We're rethinking the compiler stack around them.

Meet TIRx Harness: an open compiler harness that gives agents an environment to explore, debug, and optimize GPU kernels.

🧱 Minimal stable low-level compiler foundation for predictable GPU programming 🔍 Domain-specific compiler analysis to guide debugging and optimization 📚 Kernel zoo of 60+ kernels and hardware specs to reuse strategies and discover new ones 📊 Remote GPU evaluation for reliable benchmarks and profiling

Agent-built Kimi Delta Attention kernels achieved geometric-mean speedups of 2.94x over FlashKDA (forward) and 6.84x over FLA (backward).

Our bet: the next leap in agentic GPU programming will come from engineering the environment agents optimize in.

Check out our blog: blog.mlc.ai/2026/09/29/tir…

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Tianqi Chen @tqchenml Sep 23

Submitting your work to #MLSys2027!

Song Han @songhan_mit Sep 23

Welcome to submit to MLSys 2027. I am thrilled to serve as a PC Co-Chair this year alongside Rashmi Vinayak. We are actively looking for cutting-edge research and industry track papers at the intersection of Machine Learning and Systems. mlsys.org/Conferences/20…

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