Knowledge Base
Explore latest tutorials, guides, and articles about AI.
Neural OCR on Mobile Devices: CRAFT, CRNN, EasyOCR and ExecuTorch
How CRAFT and CRNN combine to perform neural OCR, which image size limits to expect on smartphones, and how EasyOCR-style models can be ported to on-device inference with ExecuTorch.
Read ArticleQwen 3.8 27B: 4-bit Quantization, vLLM Tensor Parallelism, and VRAM/Context Analysis
A technical guide to loading Qwen 3.8 27B with vLLM tensor parallelism, calculating VRAM usage for weights and KV cache, and estimating max context on 2x RTX 3090 and 2x RTX 5090.
Read ArticleQwen 3.8 27B: The New Open-Weight Powerhouse for Local AI
Explore the technical architecture, company background, benchmark improvements, and local deployment potential of Alibaba's Qwen 3.8 27B model.
Read ArticleDeepSeek V4 Flash 0731 vs Pro 0813: Post-Training Update Comparison
A detailed comparison of DeepSeek's V4 Flash 0731 and Pro 0813 models, focusing on benchmarks, costs, coding efficiency, and model size.
Read ArticleGemini 3.7 Flash: Efficiency Meets Intelligence in Production AI
Google's new Gemini 3.7 Flash model offers cutting-edge coding and agentic performance at a discounted price, challenging GPT-5.6 Terra and DeepSeek V4-Pro.
Read ArticleUnderstanding AI Vision: From Image Classification to Instance Segmentation
Explore the differences between image classification, object detection, semantic segmentation, and instance segmentation, and their real-world applications.
Read ArticleFrom Text to Speech: How AI Models Convert Graphemes to Audio
Explore the pipeline of text-to-speech synthesis, from grapheme-to-phoneme conversion to mel-spectrograms and vocoders, and see how Kokoro exemplifies efficient AI-driven speech generation.
Read ArticleQwen 3.8 Max and 27B: Open-Weight AI Models Compared
Comparison of Qwen 3.8 Max and 27B with Kimi K3, focusing on parameters, costs, and local deployment.
Read ArticleWhisper Model for Edge AI on Mobile
Learn how OpenAI's Whisper ASR model runs on mobile devices, comparing model sizes, language variants, and deployment techniques like ExecuTorch and CoreML.
Read ArticleOffline LLMs on Mobile: Qwen 3.5, Qwen 2.5, and Llama SpinQuant Compared
A practical guide to running small quantized LLMs on mobile devices, comparing model families, RAM usage, and quantization techniques.
Read ArticleDeepSeek V4 Flash 0731: The Most Cost-Efficient AI Model
Discover the advanced agentic performance and aggressive pricing of DeepSeek V4 Flash 0731, released July 31, 2026.
Read ArticleCNN vs. Transformer: Global Context and NVIDIA DLSS
Explanation of the differences between CNNs and Transformers, why Transformers offer better global context, and how NVIDIA leverages this in DLSS.
Read ArticleGGUF, 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.
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