#CUDA profiling
9 related articles
·6 min
AI Application Engineer Interview Guide: Core Topics on Model Quantization and Inference Deployment
A systematic guide to must-know AI application engineer interview topics: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
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·6 min
AI Application Engineer Interview Guide: Core Topics in Model Quantization and Inference Deployment
A systematic review of must-know topics for AI Application Engineer interviews: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
Read more →
·7 min
Alibaba Open-Sources 2.4T Qwen Model as Chinese AI Sweeps the Global Competitive Landscape
Alibaba open-sources a 2.4 trillion parameter Qwen model and launches the Qwen Token Plan. Chinese models surge, Kimi K3 tops global rankings, and China's AI is reshaping the global competitive landscape.
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·3 min
Is Your Training Job Quietly Slowing Down? TraceML Exposes PyTorch's Hidden Performance Bottlenecks
GPU at 51% utilization — and no one noticed? See how TraceML exposes hidden PyTorch DataLoader bottlenecks, cuts training time 43% with 3 parameter changes.
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·2 min
AI Agents for SGLang Kernel Optimization: Real-World Performance Gains and Engineering Methodology
SGLang's team converted expert knowledge into agent skills, achieving 71.4% throughput gains, TTFT reduced from 456ms to 168ms. A deep dive into agent-assisted kernel optimization methodology.
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NVIDIA Nsight Tools in Practice: A Per…
·3 min
NVIDIA Nsight Tools in Practice: A Performance Optimization Guide for Neural Reconstruction Pipelines
Learn how to optimize neural reconstruction pipelines using NVIDIA Nsight Systems and Nsight Compute — from CPU-GPU sync blocking to kernel-level profiling for autonomous driving simulation.
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·4 min
Getting Started with Huawei Ascend C Operator Programming: Kernel Functions and Hands-On Development Guide
A systematic guide to Huawei Ascend C operator programming covering kernel functions, three-stage pipeline paradigm, API categories, and a hands-on AddCustom operator walkthrough.
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Research·2 min
Optimize Anything: One API to Unify Optimization of Code, Prompts, and Agent Architectures
UC Berkeley and Stanford propose Optimize Anything, a universal text optimization framework that unifies optimization of CUDA kernels, agent architectures, and prompts through one declarative API.
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Tutorials·1 min
NVIDIA CUDA Tile: A Detailed Look at the New Tile-Based GPU Programming Paradigm
In-depth analysis of NVIDIA's new CUDA Tile programming model: how tile-based GPU kernel development integrates with C++, optimizes data locality, and accelerates Tensor Core computation for HPC, deep learning, and AI inference.
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