#CUDA C++

71 related articles

Jul 13, 2026·4 min

6 Months of Self-Taught Machine Learning: A Complete Roadmap from Zero to Job-Ready

A self-learner completed a full progression from math foundations and core ML to deep learning in 6 months—hand-writing a Transformer and implementing gradient boosting from scratch. This article breaks down the highlights and blind spots of this real roadmap.

Jul 11, 2026·7 min

Practical RL Compute Optimization: Core Strategies to Boost CPU and GPU Utilization

An in-depth look at why CPU and GPU utilization is low in RL training, covering vectorized environment parallelism, distributed Actor-Learner architectures, GPU-side simulation (Isaac Gym/Brax), and Ray RLlib practice.

Jul 11, 2026·3 min

Connecting Codex to Ollama: A Complete Guide to Deploying AI Large Models Locally

A step-by-step guide to combining Codex with Ollama to deploy open-source AI large models locally. Private data, no subscription, offline operation, no VPN needed. Includes hardware selection and setup.

Jul 11, 2026·4 min

Demystifying Unified Memory Architecture: How Mini PCs Run 70B Large Models

Why can a mini PC with unified memory run a 70B model while an RTX 4090 can't? A deep dive into the VRAM wall and unified memory architecture for smarter local AI hardware choices.

Jul 11, 2026·7 min

50 AI Research Job Applications to FAANG, 0 Replies? Unpacking the Truth Behind the Job Search Struggle

An AI research engineer with 3 years of experience sent 50 applications to FAANG with zero replies. This article breaks down the hidden barriers of top-tech AI roles, the truth about LinkedIn ghost jobs, and the MLE vs. Research Engineer divide.

Jul 10, 2026·6 min

Samsung's Chip Profit in a Single Year Exceeds 40 Years Combined: The AI Wave Triggers a Memory Price Crisis

Samsung's chip division is set to earn more profit in one year than in its past 40 years combined, with quarterly profit surging 19x YoY, surpassing NVIDIA. AI data centers are seizing HBM and DRAM capacity, driving up DDR5 and SSD prices.

Jul 10, 2026·3 min

GBrain Local Deployment: 12-Step Deep Retrieval Pipeline Outperforms Traditional RAG by 31%

GBrain's 12-step deep retrieval pipeline and knowledge graph construction outperforms traditional RAG by 31% — with full local offline deployment for data security and lower API costs.

Jul 10, 2026·6 min

Dify Local Deployment in Practice: A Complete Guide to Building an AI Application Platform with Docker

A complete guide to Dify local deployment: from Docker environment setup, source code pulling, and container startup to first access. Build a private AI app development platform across Linux, Windows, and Mac for fast enterprise AI deployment.

Jul 9, 2026·4 min

Unsloth v0.1.463-beta: Fixes Studio Crash and llama-server Permission Issue

Unsloth v0.1.463-beta fixes a Studio crash caused by access-denied errors during llama-server service discovery. Improves stability for multi-user servers and Windows environments.

Jul 8, 2026·7 min

Ollama + Hermes Local AI Deployment: Building a 100% Private Personal AI Operating System

Build a fully private local AI system with Ollama + Hermes: zero cost, no rate limits, data stays local. Learn deployment steps, model selection tips, and private/cloud hybrid workflows.

Jul 8, 2026·6 min

AI for Beginners: A Five-Step Practical Path That Skips the Math and Python Syntax Traps

Struggling with math and Python when learning AI from scratch? This article lays out a five-step entry path: grasp the concepts, learn Python lightly, master ML and deep learning principles, get hands-on with PyTorch, then deepen understanding through real projects.

Jul 8, 2026·5 min

Python Learning Roadmap for Beginners: A Three-Stage System to Avoid 90% of Detours

Learning Python from scratch? This article breaks down the three learning stages—Fundamentals, Intermediate, and Practice—covering variables, OOP, scraping, and data analysis to help you plan a systematic Python path.

The Complete Guide to Local LLM Deploy…

Jul 7, 2026·2 min

The Complete Guide to Local LLM Deployment: Tools, Hardware, and Model Selection

A complete guide to locally deploying open-source LLMs: covering VRAM requirements, quantization, tools like Ollama and LM Studio, and model selection tips for Llama, Qwen, and more.

Jul 7, 2026·3 min

Unsloth Major Update: DiffusionGemma & Gemma 4 MTP Support, 2x Inference Speed Boost

Unsloth v0.1.464-beta adds DiffusionGemma, Gemma 4 MTP, and MiniMax-M3 support, delivering ~2x inference speed boost, new Hub, RAG Q&A, tensor parallelism, and full CUDA/ROCm/Windows coverage.

Jul 7, 2026·1 min

Unsloth v0.1.47-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM

Unsloth v0.1.47-beta is out. This 67.9k-star open-source framework fine-tunes Llama, Mistral, and Qwen 2x faster with 70% less VRAM on consumer GPUs.

Jul 7, 2026·6 min

14x Speedup: How Manticore Rebuilt the ONNX Embedding Inference Path

Manticore Search restructured its ONNX inference path to achieve 14x faster text embeddings. Deep dive into batching, session reuse, zero-copy memory, and thread tuning for vector search systems.

Jul 6, 2026·2 min

Ollama: The 175K-Star Tool for Running Local LLMs with One Command

Ollama is an open-source local LLM runner with 175K+ GitHub Stars. Built in Go, it supports Llama, Mistral, Qwen and more — deploy in 3 steps, no setup headaches.

Jun 27, 2026·3 min

Qwythos-9B Hands-On Review: Deploying an Uncensored Open-Source Model That Inherits Claude's Reasoning Abilities

Hands-on review of Qwythos-9B, distilled from 500M+ Claude reasoning traces. Supports 1.04M token context, uncensored, runs on just 4GB VRAM. Full deployment guide included.

Jun 27, 2026·4 min

The Complete Guide to Running GLM-5.2 Locally with 4 RTX 3090s for $4,000

How to build a local AI inference server with 4 used RTX 3090 SXM4 GPUs to run GLM-5.2 via Llama.cpp and Unsloth IQ quantization, with real benchmarks on speed and quality.

Jun 23, 2026·3 min

DAQIRI Platform Explained: Deep Integration of High-Speed Data Acquisition and Real-Time AI Inference

Deep dive into how the DAQIRI platform embeds NVIDIA GPU-accelerated computing into high-speed data acquisition pipelines, enabling real-time AI inference for industrial inspection, scientific experiments, and autonomous driving.