Fast and Memory-Efficient Exact Attention for Large Headdim, 1.5x~6x speedup over PyTorch SDPA.
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Updated
Aug 12, 2026 - Python
Fast and Memory-Efficient Exact Attention for Large Headdim, 1.5x~6x speedup over PyTorch SDPA.
⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.
General Matrix Multiplication using NVIDIA Tensor Cores
Minimal FlashAttention in CUDA C++/CuTe: readable WMMA/CuTe kernels, no NxN workspace, up to 4.5x faster than naive PyTorch
Lynn 原生 LLM 推理引擎 · W4A8/NVFP4 量化 · 自写 CUDA/Triton kernel · MoE · 投机解码 | Lynn-native LLM inference engine for NVIDIA Blackwell
Handwritten Flash Attention 2 CUDA kernel for Blackwell (SM120) with TMA, swizzle, double buffering & warp specialization
CUDA matrix multiplication benchmarking on Jetson Orin Nano. Four implementations, three power modes, five matrix sizes. 99.5% mathematical validation. C++/CUDA and Python.
Hand-written NVFP4 W4A16 CUDA kernels and chain-MTP speculative serving — Qwen3.6-27B at up to 366 tok/s on four Tesla V100s, hardware with no FP4 support
Vulkan & GLSL implementation of FlashAttention-2
CUDA 12-first backend inference for Unsloth on Kaggle — Optimized for small GGUF models (1B-5B) on dual Tesla T4 GPUs (15GB each, SM 7.5)
Base-13 FP4 limbs for Ozaki-scheme DGEMM emulation on FP4 tensor cores
A from-scratch nano-Triton and nano-Helion: the modern GPU-kernel DSL stack, rebuilt in ~4,000 lines
Compiler MVP that detects Transformer fusion patterns, generates optimized CUDA kernels with WMMA Tensor Cores, and executes them on real GPU hardware — 10.5 TFLOPs on RTX 2070, correctness validated against PyTorch.
The only deployed sparse FP4 GEMM on SM120: beats CUTLASS 80b on every shape, wins end-to-end request latency in 81 of 112 serving regimes vs dense NVFP4.
Low-bit LLM inference engine in Rust + CUDA. Converts models to a 4-bit .wstone format (3.75x smaller) and runs them on consumer GPUs. Built for memory-bandwidth-bound decode on Turing, where bytes per weight — not TOPS — sets token throughput.
A benchmarking framework for correlators of FX telescope arrays
1,100+ FPS YOLOv8/YOLO11 inference on a $400 GPU — every kernel hand-written in one CUDA file. No cuDNN, no TensorRT, no Python at runtime. All scales, all task heads, same detections.
FlashAttention v1 forward pass in CUDA for NVIDIA Turing (SM75)
Production-grade FlashAttention FP8 e4m3 forward kernel for NVIDIA Blackwell consumer GPUs (sm_120a, e.g. RTX PRO 6000). 647–652 TFLOPS at hd=128, sl=8192. Multi-kernel dispatcher, C library with Go and Python bindings
Drop-in exact bf16 flash-attention for CUDA with a deterministic backward, tuned for Blackwell (sm_120 / RTX 5090).
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