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klotz: quantization* + machine learning*

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1. How LLM Inference Works

   A deep dive into the process of LLM inference, covering tokenization, transformer architecture, KV caching, and optimization techniques for efficient text generation.

   2025-11-26 Tags: llm, inference, transformer, tokenization, kv cache, quantization, deep learning, machine learning, neural networks by klotz
2. Inside GPT-OSS: OpenAI’s Latest LLM Architecture

   An in-depth look at the architecture of OpenAI's GPT-OSS models, detailing tokenization, embeddings, transformer blocks, Mixture of Experts, attention mechanisms (GQA and RoPE), and quantization techniques.

   2025-09-27 Tags: llm, gpt-oss, openai, transformer, mixture of experts, moe, attention, gqa, rope, quantization, machine learning, .qwen3–30b-a3b. by klotz
3. Why Your RAG Embeddings Are Costing You a Fortune (And How I Fixed It)

   This article details the often overlooked cost of storing embeddings for RAG systems, and how quantization techniques (int8 and binary) can significantly reduce storage requirements and improve retrieval speed without substantial accuracy loss.

   2025-04-30 Tags: rag, embedding, vector database, transformers, llm, quantization by klotz
4. Introducing sqlite-vec v0.1.0: a vector search SQLite extension that runs everywhere

   Introducing sqlite-vec, a new SQLite extension for vector search written entirely in C. It's a stable release and can be installed in multiple ways. It runs on various platforms, is fast, and supports quantization techniques for efficient storage and search.

   2024-08-04 Tags: sqlite-vec, sqlite, vector search, embeddings, quantization, database, ann, knn, machine learning by klotz

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