# Kernel Optimization

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Engineering at Meta Nov 11, 2026
Meta moves GPU execution control into Triton: TLX lets production training and inference orchestrate warp groups directly
Meta released Triton Low-Level Extensions (TLX), which lets engineers explicitly orchestrate warp groups, asynchronous data movement and cluster execution while keeping Triton's programming model. Modern GPU features such as warp specialization, tensor memory and cluster execution were previously either hidden behind compiler abstractions or required large amounts of architecture-specific CUDA code. TLX is already in production across Meta's recommendation training, recommendation inference and large-scale LLM pretraining, where GEM training reached 20-25% end-to-end MFU on several thousand NVIDIA Blackwell GPUs.
Meta released Triton Low-Level Extensions (TLX), which lets engineers explicitly orchestrate warp groups, asynchronous data movement and cluster execution while keeping Triton's programming model. Modern GPU features such as warp specialization, tensor memory and cluster execution were previously either hidden behind compiler abstractions or required large amounts of architecture-specific CUDA code. TLX is already in production across Meta's recommendation training, recommendation inference and large-scale LLM pretraining, where GEM training reached 20-25% end-to-end MFU on several thousand NVIDIA Blackwell GPUs.
Triton Low-Level Extensions (TLX)Multiple Instructions, Multiple Warps (MIMW)Kernel Optimization Kernel Fusion
arXiv Oct 28, 2026
An agent loop teaches itself a strange NPU: 9.293x geomean on three Gemma-4-12B decode kernels in 12 days
Frontier large language models write correct CUDA kernels for mature platforms, yet their accuracy collapses on accelerators whose architecture and programming language they have rarely seen. A multi-agent loop on the FuriosaAI RNGD NPU first builds its own target knowledge, a checked summary of the DSL documentation, chip facts measured by microbenchmarks and compiler-derived legality rules, then searches whole-kernel designs and small changes in per-kernel Design Trees. After 12 days the three Gemma-4-12B decode kernels reach a 9.293 geomean speedup over the official baseline, each within 1.25 of the cycles needed just to move its weights.
Frontier large language models write correct CUDA kernels for mature platforms, yet their accuracy collapses on accelerators whose architecture and programming language they have rarely seen. A multi-agent loop on the FuriosaAI RNGD NPU first builds its own target knowledge, a checked summary of the DSL documentation, chip facts measured by microbenchmarks and compiler-derived legality rules, then searches whole-kernel designs and small changes in per-kernel Design Trees. After 12 days the three Gemma-4-12B decode kernels reach a 9.293 geomean speedup over the official baseline, each within 1.25 of the cycles needed just to move its weights.
Byonggon Chun（First author, Corresponding author）
Multi-Agent Kernel-Optimization Loop FuriosaAI RNGD NPU furiosa-opt Rust DSL Kernel Optimization

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Meta moves GPU execution control into Triton: TLX lets production training and inference orchestrate warp groups directly

An agent loop teaches itself a strange NPU: 9.293x geomean on three Gemma-4-12B decode kernels in 12 days