Hardware Corner

Xiaomi has shown a prototype AI workstation called the Xiaomi AI Cube, a compact 150W system that combines three of the company’s XRING processors in one machine. For local LLM users, the headline number is 1.22 TB/s of bandwidth on the XRING O100 accelerator, alongside support for large local models and a system design built around three different chips. The specifications s...

Qwen3.8 27B is another model in the 27B class that looks particularly interesting for local inference. We wanted to find out what it actually takes to run it on consumer hardware, especially at longer context lengths. This article is based on our own llama.cpp benchmark results. We are not evaluating model intelligence, coding quality, or reasoning accuracy here. The focus is...

Meta’s Muse Glimmer 30B has been getting attention in local inference circles, and we wanted to know what it actually takes to run it on your own hardware. This article is not a review of the model’s reasoning or coding quality. We are strictly looking at hardware behavior. VRAM usage, context scaling, prompt processing speed, and token generation speed. The goal is simple:

If you want to stay in the Apple ecosystem and need a compact system for running local large language models, the Mac Mini with the M4 Pro and 48 GB of unified memory is currently the lowest configuration that makes practical sense. Apple also offers 24 GB versions of the Mac Mini, but they are difficult to recommend for local LLM use. Unified memory is shared between the CPU...