Quantization formats compared: GGUF vs GPTQ vs AWQ vs NF4

<!-- SC_OFF --><div class="md"><p>I applied video compression to LLM inference and got <strong>10,000x less quantization error at the same storage cost</strong></p> <p><a

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s5cggh/google_turboquant_blew_up_for_kv_cache_heres/"> <img alt="Google TurboQuant blew up for KV cache.

<h1> Complete Guide to llm-d CNCF Sandbox — Kubernetes-Native Distributed LLM Inference Framework </h1> <p>At KubeCon Europe 2026 in Amsterdam, IBM Research, Red Hat, and Google

<p>If you run local models on a Mac, you eventually hit the same fork in the road: the same model is available as a GGUF file and as an MLX version, and something has to tell you

<p>In the first eight weeks of 2026, ten major open-weight LLM architectures were released.</p> <p>GLM-5 matched GPT-5.2 and Claude Opus 4.6 on benchmarks. Step 3.5 Flash

<p>At the International AI Summit in Dublin, OpenAI Chief Financial Officer Sarah Friar took the stage to announce that the lab is holding back a breakthrough model. According to

<blockquote> <p><em>Originally published at <a href="https://llmkube.com/blog/turboquant-m5-max-quality-and-asymmetric" rel="noopener

arXiv:2605.00422v1 Announce Type: cross Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder