The 2026 Mac mini comes with an M6 or an M5 Pro chip, and Apple pitches both at local AI. Its product page says a Neural Accelerator in each GPU core "speeds up tasks like running on-device AI models", and it names Ollama and LM Studio Bionic among the apps it is built for. If you want a small, quiet box for running language models at home, it is the obvious candidate. The … [Read more...] about Mac mini M6 for Local AI: Which Configuration to Buy, and Why 16GB Is Slower
Local AI & Tech Guides
Practical guides to running AI on your own hardware. We cover local LLMs end to end — how much RAM and VRAM you actually need, which runner to use, how quantization changes the maths, and which GPUs, Macs and handhelds are worth buying. Every speed figure here is a clearly labelled estimate, never a lab claim.
Pair these guides with our free AI tools: the VRAM calculator, the hardware matrix and the model directory.
LM Studio Bionic: What It Does, What It Costs, and What Stays Local
LM Studio Bionic is LM Studio's AI agent: an app that can work through a codebase, edit documents and research a topic, using open models. It launched on 16 July 2026 as a separate app from LM Studio, and its free plan runs models on your own hardware. Whether your work stays on your machine depends on where each session's model runs, and on two details in the documentation … [Read more...] about LM Studio Bionic: What It Does, What It Costs, and What Stays Local
llmfit: How the Local LLM Fit Checker Works, and How Far to Trust It
llmfit answers the first question everyone asks about local AI: which models will actually run on this machine? It is a free, MIT-licensed command-line tool that detects your CPU, RAM and GPU, then ranks hundreds of open models by how well they fit and how fast they should run. It has nearly 38,000 stars on GitHub and has shipped a new release every week or two since August. … [Read more...] about llmfit: How the Local LLM Fit Checker Works, and How Far to Trust It
How to Run GLM-5.3-Flash Locally: 321B, MIT Licence, and the Memory It Takes
GLM-5.3-Flash is Z.ai's newest open model, released in August 2026 under a plain MIT licence. It is also big. With 321 billion parameters, of which 18 billion are active per token, it needs roughly 160GB for a usable 4-bit build, so most 128GB machines can only run it at 1 or 2 bits. This guide covers what it needs, which machine runs which build, the one Ollama surprise to … [Read more...] about How to Run GLM-5.3-Flash Locally: 321B, MIT Licence, and the Memory It Takes
Why Modern Online Slots Feel Different Even When the Basic Format Looks the Same
Most online slots seem to have a similar design: reels, symbols, animation, and buttons. However, the playing experience is often very different. Games will differ based on the interaction between the mathematical models, interface, theme, sound, and additional mechanics. Understanding these components can help users look beyond visual presentation and recognize why two … [Read more...] about Why Modern Online Slots Feel Different Even When the Basic Format Looks the Same
How to Run Qwen3.8-Flash-Next Locally: Memory, Speed and the Licence Catch
Qwen3.8-Flash-Next is the most interesting open model of the autumn for anyone with a big-memory machine. It has 180 billion parameters in total, but only 6 billion are active for each token, so it can generate text quickly on hardware that would crawl through a dense model of the same size. Qwen calls it an experimental preview of the architecture behind Qwen4. This guide … [Read more...] about How to Run Qwen3.8-Flash-Next Locally: Memory, Speed and the Licence Catch
DGX Spark vs Strix Halo for Local AI: Same Memory Bus, Very Different Price
NVIDIA's DGX Spark and AMD's Ryzen AI Max ("Strix Halo") machines are pitched at the same buyer: someone who wants 128GB of memory for local AI in a box that fits on a desk. On the spec sheet they look like different classes of machine, a Grace Blackwell "AI supercomputer" against a laptop-class APU. For running language models the comparison is closer than that, because both … [Read more...] about DGX Spark vs Strix Halo for Local AI: Same Memory Bus, Very Different Price
Two 16GB Graphics Cards Can Be 3.3x Apart for Local AI
The quick answer Twelve graphics cards in our database carry 16GB of VRAM. Their memory bandwidth runs from 288 GB/s to 960 GB/s — a 3.3x spread at identical capacity. On the same model, that is roughly 38 estimated tokens per second against 127. The GB number on the box tells you which models will load. It tells you nothing at all about how fast they will run. Those are two … [Read more...] about Two 16GB Graphics Cards Can Be 3.3x Apart for Local AI
AI Upscale Models in 2026: Which Still Ship, and Which You Cannot Sell
The quick answer The upscale models almost every Stable Diffusion tutorial recommends have stopped being developed. Real-ESRGAN has 36,976 stars on GitHub and has not received a commit in 789 days. ESRGAN, SwinIR and BSRGAN are all quiet too. None of them is archived, so nothing on the page tells you. That is less alarming than it sounds, and the reason is the useful part of … [Read more...] about AI Upscale Models in 2026: Which Still Ship, and Which You Cannot Sell
ControlNet Models in 2026: Which Ones Still Ship, and What They Weigh
Quick answer 🔴 The original ControlNet repository has not been touched since February 2024 and has made zero commits in 90 days. Nor have its author's other projects — Fooocus, Forge and ControlNet-v1-1-nightly are all dormant too, about 105,000 stars between them. ControlNet itself is not dead. It moved from one reference implementation to independent model authors, and … [Read more...] about ControlNet Models in 2026: Which Ones Still Ship, and What They Weigh