Efficient Triton Kernels for LLM Training
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Updated
Oct 27, 2026 - Python
Efficient Triton Kernels for LLM Training
FlagGems is an operator library for large language models implemented in the Triton Language.
LLM notes, including model inference, transformer model structure, and llm framework code analysis notes.
Puzzles for learning Triton, play it with minimal environment configuration!
RapidLLM is an LLM inference framework with continuous batching, tensor and data parallelism, quantized weights, and with pluggable Triton/CUDA kernels.
Tiled Flash Linear Attention library for fast and efficient mLSTM Kernels.
Production inference for encoder models - ColBERT, GLiNER, ColPali, embeddings etc. - as vLLM plugins for online and in-process deployment
Open-source inference engine that runs language, vision, speech, image and video models from one container format, with a PyTorch and a Triton compute branch. Released for NVIDIA GPUs and CPU; Apple Silicon in progress.
A "standard library" of Triton kernels.
High-performance late-interaction retrieval engine for on-prem AI. ColBERT/ColPali multi-vector search with Rust fused MaxSim, Triton GPU kernels, ROQ quantization, LEMUR routing, WAL-backed CRUD, and a FastAPI server — single machine, CPU or GPU.
Manifold-Constrained Hyper-Connections with fused Triton kernels for efficient training
Educational resource demonstrating common GPU programming pitfalls and solutions using Triton kernels.
Fast Cartesian tensor operations and equivariant interatomic potentials for atomistic simulations
A from-scratch nano-Triton and nano-Helion: the modern GPU-kernel DSL stack, rebuilt in ~4,000 lines
Official Code for the paper ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces (in ICML 2025)
KernelHeim – development ground of custom Triton and CUDA kernel functions designed to optimize and accelerate machine learning workloads on NVIDIA GPUs. Inspired by the mythical stronghold of the gods, KernelHeim is a forge where high-performance kernels are crafted to unlock the full potential of the hardware.
Triton Kernel Tutorials for low level LLM optimization
Constraint-aware evolutionary optimization for Triton kernels on Ascend NPU — national third prize
LLM agents that generate, verify, and evolve Triton GPU kernels. Includes a reward-hack-resistant benchmarking harness with strict correctness verification and fresh-input evaluation. Achieves up to 174.7× over PyTorch eager and outperforms FlexAttention (1.48×) and SDPA (1.17×) on selected workloads.
A container of various PyTorch neural network modules written in Triton.
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