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Explore latest tutorials, guides, and articles about AI.
GGUF, EXL2, GPTQ, and AWQ: A Comprehensive Comparison for LLM Serving
Learn the key differences between GGUF, EXL2, GPTQ, and AWQ quantization formats and how they are used in llama.cpp and vLLM for home and enterprise users.
Read ArticleThe Importance of FP8 Hardware Support in AI Inference
Learn why native FP8 hardware support is crucial for AI inference, how it doubles TFLOPS, and which hardware (NVIDIA, AMD, Intel) currently supports it.
Read ArticleBF16 vs FP16 in AI Training: Precision and Stability
This article explains the difference between BF16 and FP16 in AI training, why BF16 prevents training collapse, and when to use each format.
Read ArticleSpeed and Precision Trade-offs in AI Quantization
Explains the speed and precision trade-offs in AI quantization: INT4 vs mixed precision and INT8 vs FP8.
Read ArticleUnified Architecture Showdown: AMD Strix Halo vs Apple M3/M4/M5 Max for LLM Inference
Compares AMD Strix Halo with 128GB RAM and Apple M3 Pro/Max, M4 Pro/Max, M5 Pro/Max in terms of memory bandwidth, prompt processing (prefill) speed, and decoding (token generation) speed for local large language model inference.
Read ArticleGPU vs Apple Silicon: RTX 3090/4090/5090 TFLOPS vs M3/M4/M5 Max — LLM Inference Performance Comparison
Detailed comparison of raw compute performance, prompt processing, decoding speed, memory capacity, power consumption, and portability between NVIDIA RTX GPUs and Apple MacBook Pro M-series Max chips for local LLM inference.
Read ArticleComparing NVIDIA DGX Spark and AMD Strix Halo for Local AI
A detailed comparison of the NVIDIA DGX Spark and AMD Strix Halo (Ryzen AI Max+) covering memory bandwidth, prefill and decode performance, matrix operation support, model compatibility, price, and power.
Read ArticlePrompt Processing vs. Decoding: Compute Power vs. Memory Bandwidth in AI Inference
Learn why prompt processing (prefill) depends on TFLOPS and decoding depends on memory bandwidth – and how to optimize your AI infrastructure accordingly.
Read ArticleUnderstanding Q, K, V Vectors in Transformer Models
A comprehensive explanation of Q, K, and V vectors in Transformer models, including derivation, self-attention mechanism, prompt processing, inference with KV cache, and multi-head attention with all-reduce.
Read Article2x 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.
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