#GPU kernel tuning
94 related articles
Product Reviews·1 min
NVIDIA CompileIQ: Auto-Tuning Compiler Options to Squeeze Every Drop of GPU Kernel Performance
Deep dive into NVIDIA CompileIQ auto-tuning tool that intelligently searches compiler option combinations to find optimal build parameters for CUDA kernels.
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·2 min
Reverse Engineering Apple's ANE: Open-Source Project Enables On-Device Training on the Neural Engine
Deep dive into the maderix/ANE GitHub project that reverse engineers Apple's private APIs to enable neural network training on the Apple Neural Engine, exploring its technical approach, efficiency gains, compliance risks, and implications for on-device AI.
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·3 min
GitHub Daily · July 29: Voice AI and On-Device Training Take Flight as Open-Source Alternatives Rise
GitHub Trending July 29: Microsoft's VibeVoice leads voice AI open-source wave, MoonshotAI's FlashKDA CUDA kernel surges 25%, and open-source alternatives rise.
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·3 min
DeepSeek-OCR Deployment & Fine-Tuning in Practice: From vLLM Inference to Unsloth Fine-Tuning
Complete guide to DeepSeek-OCR from vLLM inference deployment and Unsloth model loading to fine-tuning, covering cloud server setup, GPU selection, and code examples — all on a single 4090 GPU.
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·7 min
DIY Companion Robot: Building Your AI Desktop Buddy with Jetson Orin
A maker builds a DIY companion robot with NVIDIA Jetson Orin and 4S LiPo battery. Explore the full development journey from first power-up to AI interaction, including edge computing, power design, and companion robot trends.
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·3 min
DeepSeek-OCR Deployment and Fine-Tuning: A Complete Hands-On Guide
Master the full DeepSeek-OCR deployment and fine-tuning workflow: vLLM inference deployment, efficient Unsloth fine-tuning, dataset preprocessing, LoRA training, validation, and RAG vector database integration.
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·3 min
Jensen Huang Backs Chinese Open-Source AI: Great Models Deserve to Be Widely Used
NVIDIA CEO Jensen Huang says US companies should absolutely be allowed to use Chinese open-source AI models like DeepSeek and Kimi, calling backdoor fears a misunderstanding and arguing great models drive more compute demand.
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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.
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·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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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·6 min
Tongyi Qianwen Qwen-Image-3.0: The Leap of AI Image Generation from Toy to Productivity Tool
Tongyi Qianwen Qwen-Image-3.0 image generation model gets a comprehensive upgrade: supporting 4,500-token ultra-long instructions, pixel-level detail rendering, 12-language knowledge understanding, and ancient painting restoration. This article analyzes its three core capabilities.
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Handwritten C/CUDA Inference Engine: P…
·2 min
Handwritten C/CUDA Inference Engine: Pushing Qwen 35B to the Limits on RTX 5090
A deep dive into a handwritten C/CUDA inference engine for Qwen 35B on RTX 5090 (Blackwell), covering quantization, FlashAttention kernels, and memory optimization.
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GitHub Daily · July 19: The Dual Advan…
·3 min
GitHub Daily · July 19: The Dual Advance of Heterogeneous Inference and Agent Toolchains
GitHub Trending July 19: ktransformers tops the list with heterogeneous inference optimization, while jcode, cua, and AstrBot signal a maturing Agent ecosystem.
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·4 min
The Local AI Inflection Point: Edge Deployment, Multi-Model Collaboration, and the Open-Source Ecosystem
AI Engineer Summit deep dive: Local AI hits a real inflection point, driven by privacy and cost. Multi-model collaboration goes mainstream, NVIDIA + ExoLabs achieve 10x gains, open-source ecosystem accelerates.
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Block Low-Rank Compression: A Guide to…
·2 min
Block Low-Rank Compression: A Guide to Accelerating Large Model Inference on Memory-Constrained GPUs
Learn how Block Low-Rank (BLR) decomposition compresses large model memory usage and accelerates GPU inference, including CUDA kernel optimization and combination with quantization and pruning.
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TurboVec: A Deep Dive into the Rust-Po…
·3 min
TurboVec: A Deep Dive into the Rust-Powered High-Performance Vector Index Library
TurboVec is a Rust-based vector index library powered by TurboQuant, with Python bindings for RAG, semantic search, and AI applications. A deep-dive into its architecture.
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·3 min
Complete Guide to Deploying LLMs Locally on RobotCore Mini: From Ollama Installation to Web Interface Setup
A complete guide to deploying LLMs locally on RobotCore Mini using Ollama — covering model pulling, CLI verification, Web backend setup, and LAN access.
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·3 min
GPUStack in Action: Complete Guide to Day 0 Deployment of DeepSeek-V4-Flash
Deploy DeepSeek-V4-Flash DSpark on 8× H20-141G using GPUStack's SGLang backend on Day 0. Full walkthrough of Web UI config, parameter tuning, and 200 tokens/sec benchmark results.
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4 Alternatives for Running CUDA on Non…
·3 min
4 Alternatives for Running CUDA on Non-NVIDIA Hardware: A Comparative Guide
A deep dive into running CUDA on non-NVIDIA hardware (AMD, Intel): comparing ROCm/HIP, ZLUDA, SYCL/oneAPI, and OpenCL across principles, use cases, and limitations.
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