Knowledge Base
Explore latest tutorials, guides, and articles about AI.
What 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.
Read ArticleRunning AI on Consumer Hardware
What you need to run AI models locally, from laptops to gaming PCs, and how to get the best performance from your hardware.
Read ArticleInference Optimization Techniques
The various techniques used to make AI models run faster, including batching, kernel fusion, and speculative decoding.
Read ArticleMatrix Multiplications and GPU Compute
Why matrix multiplication is the fundamental operation of AI, and how GPUs are purpose-built to do it extremely fast.
Read ArticleFine-Tuning vs LoRA
The difference between full fine-tuning and LoRA adapters, and when to use each approach.
Read ArticleDiffusion Models Explained
How diffusion models create images from noise, and why they are the technology behind modern AI image generation.
Read ArticleEmbeddings and Vector Representations
How AI converts meaning into numbers, and why embeddings are the foundation of search, recommendations, and RAG.
Read ArticleRetrieval Augmented Generation (RAG)
How RAG combines search with language models to give AI access to external knowledge without retraining.
Read ArticleHallucinations in AI
Why AI models make things up, when it happens, and how to detect and reduce hallucinations in practice.
Read ArticlePrompt Engineering Basics
How to craft effective prompts that get better results from AI models, with practical techniques anyone can use.
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