triton

Meta's FBTriton is a downstream fork of OpenAI's Triton compiler that enables rapid development of GPU optimizations like TLX and autoWS while staying synced upstream. This blog details its continuous upstream ingestion strategy, hierarchical L1/L2/L3 validation framework, and the practical challenges of balancing innovation with production stability.

Presents MKEvolve, a modular multi-agent framework that iteratively co-evolves modular decomposition and LLM-generated kernels for hardware accelerators, achieving improved correctness and speedup over direct synthesis while reducing token usage.

SonicSampler presents a unified suite of tile-aware Triton kernels that vertically fuse the entire LLM sampling pipeline, supporting dynamic per-request behaviors and speculative verification, achieving up to 16x speedup over state-of-the-art baselines.

Technical post detailing how to run DeepSeek V4 Flash on two Nvidia 4090d GPUs using custom Triton kernels and vLLM, achieving ~105 tokens/second with 262k context.

The PyTorch-Triton 3.7 release introduces the Triton Plugin Extensions system, enabling dynamic loading of custom compiler passes and DSL extensions into upstream Triton without forking, with Meta's TLX now supported out of the box.

Elliot Arledge explains why he prefers using a Kimi Linear megakernel over Qwen 3.6 for kernel performance, comparing parameter counts, layer synchronization, hidden dimensions, and architecture-specific optimizations. The discussion highlights that Kimi Linear architecture is more suitable for megakernel implementation, especially for batch-1 decode on RTX PRO 6000 Blackwell.

A CMU PhD who developed the kernels now used by NVIDIA in TensorRT-LLM explains fast attention, covering fused CUDA kernels, FlashInfer, Triton, and paged-KV attention, enabling more tokens per second on the same GPU.

This post explains the Decompose-K technique for accelerating skinny large-K matrix multiplications by splitting the K dimension into chunks, running batched matmuls, and summing partials. It provides a PyTorch implementation and benchmarks showing significant speedups over standard torch.compile for bad-shaped matmuls.

This blog post continues the profiling in PyTorch series, exploring nn.Linear, MLP blocks, and fusion techniques using Triton kernels to optimize performance.

A detailed guide on learning AI inference engine internals, covering serving engines like vLLM and SGLang, low-level GPU kernel programming with Triton and CUTLASS, and a sequence of mini-projects to build hands-on expertise.

Helion is a Python DSL that compiles to optimized Triton code for performance-portable GPU kernels. This tutorial at PLDI 2026 covers Helion's architecture, autotuning, and CuteDSL backend.

Meta open-sources TLX Block Attention, a warp-specialized Triton kernel that achieves 2.3x speedup for block-diagonal self-attention on NVIDIA Blackwell GPUs, with up to 3.5x speedup when fused with rotary embeddings.

FPSan is a Triton compiler pass that enables verification of algebraic equivalence of floating-point programs by replacing floating-point operations with integer operations, relying on Schanuel's conjecture for correctness.

KernelBench-X is a new benchmark for evaluating LLM-generated GPU kernels, revealing that task structure impacts correctness more than method design and that correctness does not guarantee hardware efficiency.

Researchers from Carnegie Mellon, University of Washington, and Arm propose AdaExplore, an LLM agent framework for GPU kernel code generation that achieves 3.12× and 1.72× speedups on KernelBench Level-2 and Level-3 benchmarks through failure-driven adaptation and diversity-preserving search, without additional fine-tuning.