Knowledge Base
Explore latest tutorials, guides, and articles about AI.
Transformer 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.
Read ArticleWhat is Model Quantization?
How quantization makes AI models smaller and faster, and why it is essential for running models on consumer hardware.
Read ArticleAWQ Quantization Explained
How AWQ quantization works and why it is one of the best methods for compressing AI models with minimal quality loss.
Read ArticleDifferent Quantization Methods Compared
A comparison of GPTQ, GGUF, AWQ, and other quantization methods to help you choose the right one.
Read ArticleFloating Point Formats: BF16, FP8, INT8
How different number formats affect AI model performance, memory usage, and output quality.
Read ArticleMulti-modal Models
How modern AI models understand multiple types of data including text, images, audio, and video simultaneously.
Read ArticleMemory Bandwidth: The AI Speed Limit
Why memory bandwidth is the most important factor for AI inference speed, and how it determines how fast models can generate text.
Read ArticleVRAM vs RAM vs Multi-Channel RAM
The differences between GPU memory, system memory, and why multi-channel RAM configurations matter for AI performance.
Read ArticleGPU Memory Bandwidth: HBM and GDDR
How HBM and GDDR memory technologies compare, and why data center GPUs use different memory than consumer cards.
Read ArticlePrompt Processing and GPU Speed
How the prompt processing phase works, why it differs from token generation, and how GPU compute power affects it.
Read ArticleToken Generation Speed and Bandwidth
Why token generation is bandwidth bound, and how to calculate the maximum speed of any hardware for your model.
Read ArticleContext Size Explained
What context window means, why it matters, and how it affects model performance and memory usage.
Read ArticleKV Cache and Memory Management
How the KV cache works, why it is essential for fast generation, and how it affects memory usage during inference.
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