Real-time stream editing pipeline powered by the FLUX.2-klein-4B model, optimized for consumer GPUs
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Updated
Jun 13, 2026 - Python
Real-time stream editing pipeline powered by the FLUX.2-klein-4B model, optimized for consumer GPUs
GPU Profiler for AI agents
GPU 性能与 AI Infra 学习项目:CUDA/Triton 算子、NCU/NSYS、vLLM/SGLang/TRT-LLM/ms-swift、PyTorch/DeepSpeed/ms-swift 训练、并行架构
GVProf: A Value Profiler for GPU-based Clusters
Forge kernels — fused Triton kernels for faster, leaner LLM fine-tuning. One-call patching into Hugging Face models via forge.patch(model), with FSDP2 multi-GPU support and a growing set of kernels and architectures. Every speedup backed by a committed benchmark. Apache-2.0.
KeSSie HUGE Context Semantic recall for Large Language Models
Faster attention kernels for serving TML's Inkling model on vLLM. 2.7x over the shipping path on H100, and the only implementation that runs on A100.
The GPU Optimizer for ML Models enhances GPU performance for machine learning. It offers advanced scheduling, real-time monitoring, and efficient resource management through a user-friendly web interface and robust API, integrating big data technologies for seamless data processing and model optimization. @NVIDIA
A high-performance C++ backend for extreme-context LLM inference. It replaces item-count batching with dynamic, VRAM-aware First-Fit Decreasing (FFD) bin packing. By using PyBind11 for async queueing, 16-token alignment, and `cudaHostAlloc` for zero-copy FlashAttention-2 transfers, it mathematically eliminates OOMs and maximizes GPU throughput.
Hardware-aware Ollama fork with legacy NVIDIA support, controllable runtime tuning, Q4/Q8 KV cache, Flash Attention, CUDA Graphs and optimized multi-GPU inference.
用于复现和优化常见的深度学习算子,基于cuda和triton两种方案,可供学习和参考
Executive FinOps dashboard and automated governance engine using FOCUS 1.3 standards for AWS, Azure, and Snowflake.
Physics-based computation at scale — Hamiltonian dynamics, spectral theory, and statistical mechanics powering optimization, drug discovery, genomics, molecular proof, and agentic commerce.
Comparative benchmark of 4 LLM inference engines (HuggingFace, vLLM, AWQ, Speculative Decoding) on AMD GPU. Streamlit dashboard with real-time GPU telemetry, concurrent load testing, and throughput/latency visualization.
The NVIDIA driver's fan control logic wasn't doing it for me — too conservative, too opaque — so I built my own. This is a Linux GUI application for independent NVIDIA GPU fan control without requiring Coolbits. Uses pynvml via a root helper subprocess for direct fan management.
LLM pretraining from scratch on FineWeb dataset (architecture and all components explained), plus optimal use of GPU on SLURM cluster
🚀 Achieve rapid training of NanoGPT (GPT-2 124M) on a single RTX 4090, targeting a validation loss below 3.28 with FineWeb-Edu data.
Multi-GPU parallelized YOLOv5 training and inference pipeline — data-parallel scaling and throughput tuning.
A no-cost infrastructure benchmark measuring the VRAM and throughput impact of NF4 (4-bit) quantization on LLMs
🤖 NLP pipeline for low-resource machine translation using ByT5, advanced text normalization, custom training loops, and optimized Transformer inference.
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