TinyML How Do You Run AI Models on Extremely Limited Hardware? A Deep Dive Into TinyML and Edge AI From Game Boy consoles to factory sensors, AI is escaping the data center. Learn the techniques, hardware, and software frameworks enabling machine learning on microcontrollers with just kilobytes of memory.
AI What Is Model Quantization and Which Format Should You Use for Local LLMs in 2026? Choosing between GGUF, GPTQ, and AWQ quantization formats can make or break your local LLM deployment. This data-backed guide breaks down which format works best for your hardware and use case in 2026.
Local LLM What GPU Do I Need to Run Local LLMs? A Complete Hardware Guide for 2026 VRAM is the single most important spec for local LLMs. This complete guide breaks down exactly which GPU you need—from $250 Intel Arc to RTX 4090—with real benchmarks for Llama 4, DeepSeek R1, and more.
quantization How Much Quality Is Lost When Quantizing LLMs? A Data-Driven Analysis of Q4_K_M vs FP16 Quantization makes local LLMs accessible, but how much quality do you actually lose? We analyzed benchmark data from MMLU, GSM8K, and HellaSwag to compare Q4_K_M, Q8_0, and FP16 performance.
Microsoft Microsoft Open-Sources BitNet: The 1-Bit LLM Inference Framework That Runs 100B Models on Your CPU Microsoft open-sources BitNet (bitnet.cpp), an inference framework that runs 100B parameter 1-bit LLMs on consumer CPUs with up to 6x speedup and 82% energy reduction.
