#CUDA kernel optimization
120 related articles
·3 min
LFM2.5 Local Deployment Hands-On: An 8B Parameter Model That Outperforms GPT-o3s in Tool Calling
Hands-on test of Liquid AI's LFM2.5 local deployment: architecture breakdown, 16GB VRAM troubleshooting, and GraphRAG tool-calling benchmarks vs GPT-o3s.
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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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·4 min
Cursor Composer2 Training Revealed: A Complete Guide to Distributed Reinforcement Learning Engineering
Deep dive into how Cursor trained Composer2: two-stage architecture, global distributed clusters, MOE numerical alignment, simulation anti-cheating, and more.
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·3 min
vLLM Deep Dive: How PagedAttention Enables High-Throughput LLM Inference
Deep dive into vLLM's core technologies for high-throughput LLM inference, including PagedAttention memory management, continuous batching, distributed deployment, and comparisons with TensorRT-LLM.
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Expert Opinions·3 min
Windsurf CEO Deep Dive Interview: Speed Is the Only Moat
Windsurf CEO Varun Mohan shares insights on AI coding IDE pivots, product methodology, async Agent challenges, and differentiation strategy vs Cursor. Speed is the only moat.
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Tutorials·3 min
Why Learning AI from Scratch Leaves You More Confused — A Clear, Systematic Roadmap for Beginners
Confused learning AI from scratch? This guide breaks down why fragmented learning fails and provides a complete path from Python to deep learning with practical tips.
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Tutorials·3 min
llama.cpp MTP Acceleration Deployment Guide: Configuration Steps & Real-World Benchmarks
Guide to enabling MTP multi-Token prediction acceleration in llama.cpp, covering CUDA setup, desktop configuration, model selection, and benchmarks showing ~60 Token/s with Qwen3 27B.
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Tutorials·4 min
Practical Guide to Building a Local AI Knowledge Base with Qwen3.5 + RAGFlow + Ollama
Step-by-step guide to building a local RAG knowledge base using RAGFlow, Ollama, and LM Studio with Docker, covering Embedding model deployment and network troubleshooting for private AI Q&A.
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Research·2 min
Agent Loops in Practice: Transforming Token Output into Productivity from CUDA Kernels to Automated Research
Deep dive into how the Humanize framework transforms LLM tokens into engineering productivity via Agent Loops. Covers KDA winning CUDA kernel contests, virtual hardware optimization, and 50% research cost reduction.
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Tech Frontiers·2 min
SGLang v0.5.12.post1 Released: DeepSeek V4 Stability Fixes and Blackwell Adaptation
SGLang v0.5.12.post1 stability patch details: 12 critical fixes covering DeepSeek V4 garbled text and crashes, NIXL PD disaggregated inference logic, Blackwell B300 adaptation, and cold start optimization.
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Industry Insights·2 min
AMD MI355X Beats B200: Full-Stack Optimization Breakdown for 5% Lower TCO on DeepSeek-R1 Inference
AMD Instinct MI355X achieves 5% lower TCO than NVIDIA B200 on DeepSeek-R1 disaggregated inference via SGLang+MoRI full-stack optimization with 1.25x per-GPU throughput.
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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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Industry Insights·2 min
NVIDIA Dynamo Snapshot: A Snapshot Recovery Solution for GPU Inference Cold Start Problems
Deep dive into how NVIDIA Dynamo Snapshot reduces LLM inference cold start time from minutes to seconds via GPU state snapshot and recovery, covering Kubernetes integration and elastic inference.
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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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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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Tutorials·2 min
Hermes + Qwen3.6 Local Deployment Tutorial: Build Your Private AI Agent at Zero Cost
Step-by-step tutorial to deploy Hermes Agent with Qwen3.6 open-source LLM locally. Covers WSL setup, model download, Telegram bot integration for a zero-cost private AI Agent.
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Tutorials·2 min
vLLM and SGLang Local Deployment Tutorial: A Practical Guide to 3-8x Performance Gains
Complete guide to deploying vLLM and SGLang locally. Compare performance vs LM Studio, deploy in 3 steps with Docker + AI assistant. Covers SGLang vs vLLM selection, 5090 VRAM optimization, and Cherry Studio integration.
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Tutorials·4 min
NVIDIA Model Optimizer Post-Training Quantization (PTQ) Practical Guide
A deep dive into NVIDIA Model Optimizer's PTQ workflow, covering INT8/INT4 quantization principles, calibration methods, RTX GPU optimization, and best practices for deploying quantized LLMs on consumer GPUs.
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Deep Dives·3 min
AI Model Deployment Pipeline Friction: How TensorRT Systematically Eliminates Inference Optimization Bottlenecks
Deep dive into pipeline friction in AI model deployment from training to production, covering TensorRT automated optimization, ONNX export, and Triton Inference Server best practices.
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Tech Frontiers·3 min
Mistral AI Teams Up with NVIDIA to Build Open-Source AI Models — How Will This Partnership Reshape the Industry?
Mistral AI and NVIDIA announce a strategic partnership to co-develop frontier open-source AI models. A deep analysis of the collaboration's background, technical synergies, and impact on the open vs. closed source AI landscape.
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