Linux Device Drivers 3 examples updated to work in recent kernels

FlashInfer: Kernel Library for LLM Serving

materials available to the public

📖A curated list of Awesome LLM Inference Paper with codes, TensorRT-LLM, vLLM, streaming-llm, AWQ, SmoothQuant, WINT8/4, Continuous Batching, FlashAttention, PagedAttention etc.

Script that organizes the Google Takeout archive into one big chronological folder

A large-scale simulation framework for LLM inference

backup tools for OG pixel & pixel XL

Documentation of NVIDIA chip/hardware interfaces

Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)

Awesome resources for GPUs

MLIR For Beginners tutorial

通过MicroBenchmark获悉Ampere微架构知识

[CVPR 2023 Best Paper Award] Planning-oriented Autonomous Driving

Tengine is a lite, high performance, modular inference engine for embedded device

Typescript based SystemVerilog meta-language and HDL design framework

Efficient operation implementation based on the Cambricon Machine Learning Unit (MLU) .

Compare different hardware platforms via the Roofline Model for LLM inference tasks.

CUDA on non-NVIDIA GPUs

SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime

Best inference performance optimization framework for HuggingFace Diffusers on NVIDIA GPUs.

🤘 TT-NN operator library, and TT-Metalium low level kernel programming model.

Assembler and Decompiler for NVIDIA (Maxwell Pascal Volta Turing Ampere) GPUs.

Unified compiler/runtime for interfacing with PyTorch Dynamo.

Shared Middle-Layer for Triton Compilation

Assembler for NVIDIA Maxwell architecture

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Stable Diffusion and Flux in pure C/C++