MLX vs GGUF on Apple Silicon: which local LLM format should you actually use?

<!-- SC_OFF --><div class="md"><p>Has anyone of the mac users tested the speed difference (token gen, promt processing) between mlx quants without mtp, vs gguf quants with

<p>Ask how much memory it takes to fine-tune a 7B model and the instinct is "the model's 14 GB in fp16, so a bit more than that". The real figure is about 112 GB, before you've

<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

<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

<h2> The August 2026 open-weights pivot </h2> <p>For developers and machine learning engineers running inference locally, the open-weight landscape in 2026 has often presented a

<!-- SC_OFF --><div class="md"><p>That foodtruck bench post showing deepseek v4 matching gpt-5.2 at 17x cheaper got me thinking. if frontier cloud models are that overpriced for

<!-- SC_OFF --><div class="md"><p>I have experience using llama.cpp on Windows/Linux with 8GB NVIDIA card (384 GB/s bandwidth) and offloading to CPU to run MoE models. I typically