#gguf
4 posts tagged with #gguf
Every article below is hand-written, technically reviewed, and focused on gguf. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning LLM Quantization Levels Compared: Q4_K_M vs Q8_0 vs FP16 [2026]
The practitioner's guide to choosing between Q4_K_M, Q5_K_S, Q8_0, and FP16 quantization for local LLMs — with real perplexity numbers, throughput benchmarks, and per-use-case recommendations.
![GGUF vs GPTQ vs EXL2: LLM Quantization Compared [2026]](https://img.kunalganglani.com/images/vzekdneq/production/34e429bea58e21d5119b5baad6b9efe44974be77-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning GGUF vs GPTQ vs EXL2: LLM Quantization Compared [2026]
A head-to-head comparison of GGUF, GPTQ, and EXL2 quantization formats with real quality, speed, and VRAM trade-offs — updated for the 2026 Hugging Face acquisition of ggml.ai.
Developer Tools Ollama vs llama.cpp 2026: Which Local LLM Tool Actually Wins?
Ollama wins for developers who want a fast, polished setup with REST APIs and model management. llama.cpp wins for power users squeezing every last token of performance from their hardware.
Technology Portable LLM on a USB Stick: Offline AI Setup [2026]
Run a full LLM from a USB drive with zero internet. Covers Ollama portable setup, LM Studio on external drives, USB Uncensored LLM migration, and GGUF model selection with real performance numbers.