Knowledge Base
Explore latest tutorials, guides, and articles about AI.
2x RTX 3090 with vLLM in 2026: Performance Analysis and KV Cache Strategies
Comparison of 2x RTX 3090 with vLLM in 2026: token processing, decoding speed, model sizes, and KV cache optimization with a warning about quantization for programming tasks.
Read ArticleTensor Parallelism vs. Pipeline Parallelism: AI Model Parallelization on Multiple GPUs
Explanation of the differences between Tensor Parallelism and Pipeline Parallelism in parallelizing AI models on multiple GPUs, including overheads and optimal use cases.
Read ArticleKimi K3: The New Frontier in Open-Weight AI
Kimi K3 is a new open-weight model with 2.8 trillion parameters, offering competitive pricing and impressive benchmark results.
Read ArticleCoding in 2026: Mid-Year Update on AI Efficiency and Cost
An overview of the AI coding landscape in mid-2026, focusing on cost-per-token, intelligence index, and the best models for efficiency, including DeepSeek V4, Gemini 3.5, GPT-5.6, and Meta Muse.
Read ArticleThe 2026 Memory Market: AI, Enterprise, and Chinese Competition
An overview of the memory market in 2026, highlighting the shift to enterprise AI, the rise of Chinese players, and the debate over AI spending sustainability.
Read ArticleHow HBM3 and HBM4 Work: Differences, Performance, Costs, and Future Outlook
An in-depth analysis of High Bandwidth Memory generations HBM3 and HBM4, covering architecture, speed, cost, key players, and future projections for AI computing.
Read ArticleWhat is Artificial Intelligence?
A gentle introduction to artificial intelligence, from its core concepts to how it shapes the technology we use every day.
Read ArticleNeural Networks Explained Simply
How neural networks learn, explained without the complex math, using simple analogies anyone can understand.
Read ArticleWhat Are Model Weights?
Understanding model weights, the core of what makes an AI model actually work and what it has learned.
Read ArticleBias in AI Models
What bias means in AI, how it gets into models, and why it matters for the reliability of artificial intelligence.
Read ArticleWhy GPUs Power AI
Why graphics cards became the backbone of modern AI, and what makes them so much better than CPUs for this job.
Read ArticleCUDA Explained
What CUDA is, why it matters for AI, and how NVIDIA's software platform became the standard for GPU computing.
Read ArticleTransformer Architecture Basics
How the Transformer architecture revolutionized AI, and why it became the foundation of modern language models.
Read ArticleTraining vs Inference
The difference between training an AI model and using it, and why each requires very different hardware and resources.
Read ArticleTokenization: How AI Reads Text
How AI models break text into tokens, why it matters for performance, and how it affects what models can understand.
Read ArticleAttention Mechanisms
How attention lets AI models focus on what matters, and why it is the most important concept in modern AI.
Read ArticleDense vs MoE Models
The difference between dense and mixture of experts models, and why the architecture choice matters for performance.
Read ArticleUnderstanding Model Parameters
What model parameters mean, how they relate to capability, and why bigger is not always better.
Read ArticleSmall vs Large Models
When to use a small model and when you need a large one, with practical advice for choosing the right tool.
Read ArticleFrontier Models Overview
An overview of the most advanced AI models available today, including GPT-4, Claude, Gemini, and Llama.
Read ArticleOpen Source vs Closed Source Models
The key differences between open and closed AI models, and why the choice matters for developers and users.
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