If you are building a local RAG system in 2026, the embedding model matters more than the LLM you plug it into. A weak retriever hands your model the wrong chunks, and no amount of parameters downstream will fix that. The good news is that the best open embedding models are small — most run comfortably in under 1 GB, alongside your main model, on hardware you already … [Read more...] about Best Local Embedding Models for RAG in 2026: Sizes, Dimensions & Licences
Coding
How to Run Qwen3.8 27B Locally: Hardware, VRAM & Setup Guide (2026)
Qwen3.8 27B is Alibaba's newest open-weight model, released in August 2026. It's one of the strongest models you can realistically run on your own computer. The short version: you need about 18GB for the model at 4-bit, plus room for context, so a 24GB graphics card or a Mac with 32GB or more of unified memory. This guide covers exact memory needs by quantization and context … [Read more...] about How to Run Qwen3.8 27B Locally: Hardware, VRAM & Setup Guide (2026)
AMD Strix Halo vs Apple M5 Max: The 128GB Unified Memory Local AI Showdown
If you want to run big AI models locally without buying several expensive GPUs, two platforms offer up to 128GB of fast unified memory in a small box: AMD's Ryzen AI Max+ 395 ("Strix Halo") and Apple's Max-class chips, now the M5 Max in the 2026 Mac Studio. Both can hold 70B-class models and ~120B Mixture-of-Experts models that won't fit on a consumer GPU. This guide compares … [Read more...] about AMD Strix Halo vs Apple M5 Max: The 128GB Unified Memory Local AI Showdown
Best Local LLM Runners in 2026: Ollama vs LM Studio vs Jan vs llama.cpp vs vLLM
To run an AI model on your own computer you need a runner: the software that loads the model and serves it to you or your apps. The five most popular are Ollama, LM Studio, Jan, llama.cpp and vLLM. They overlap a lot (four of them are built on or around llama.cpp), but each suits a different kind of user. This guide compares them as of September 2026 and helps you … [Read more...] about Best Local LLM Runners in 2026: Ollama vs LM Studio vs Jan vs llama.cpp vs vLLM
Running Giant Open Models Locally: DeepSeek V4, Qwen3.8 & Llama 4 Hardware Sizing Guide
The biggest open-weight models are now Mixture-of-Experts (MoE) giants with hundreds of billions, or even trillions, of parameters. Can you run them at home? Sometimes, if you understand one key idea: MoE models need memory for all their parameters, but only compute a small "active" slice for each token. This guide explains how to size hardware for today's largest open models, … [Read more...] about Running Giant Open Models Locally: DeepSeek V4, Qwen3.8 & Llama 4 Hardware Sizing Guide
Quantization Guide for Local LLMs: Q4_K_M vs Q8_0 vs FP8 (DeepSeek R1 Examples)
When you download a model for Ollama or LM Studio, you'll see labels like Q4_K_M, Q8_0 or FP8. These are quantization levels: ways of compressing a model's weights so it takes less memory and runs faster, at some cost to accuracy. This guide uses the DeepSeek R1 distills as examples, but the same rules apply to Qwen, Gemma, gpt-oss and other models. It explains what each format … [Read more...] about Quantization Guide for Local LLMs: Q4_K_M vs Q8_0 vs FP8 (DeepSeek R1 Examples)
NVIDIA RTX 5080 vs RTX 4090 for Local LLMs: Why 16GB VRAM Is the Limit
On paper, NVIDIA's RTX 5080 is the newer, more efficient card. For gaming it's roughly in the RTX 4090's league. For local AI, though, the comparison comes down to one number: 16GB of VRAM on the 5080 versus 24GB on the 4090. That 8GB gap decides which models you can run entirely on the GPU. This guide explains the practical difference and what it means for buyers in 2026, when … [Read more...] about NVIDIA RTX 5080 vs RTX 4090 for Local LLMs: Why 16GB VRAM Is the Limit
How to Run Qwen Coding Models Locally in VS Code & Cursor (Free Copilot Alternative)
You can get a capable AI coding assistant without a subscription, and without sending your code to the cloud, by running an open Qwen coding model locally and connecting it to your editor. This guide shows how to do it in VS Code with Continue or Cline, explains what works (and what doesn't) in Cursor, and helps you pick a model your hardware can handle in 2026. Illustration: … [Read more...] about How to Run Qwen Coding Models Locally in VS Code & Cursor (Free Copilot Alternative)
Best Mac for Local LLMs in 2026: Mac mini M6 & M5 Pro vs Mac Studio M5 Max & Ultra
Macs are among the easiest and most cost-effective ways to run large AI models locally, because Apple Silicon's unified memory lets the GPU use most of the system RAM. A 64GB or 128GB Mac can hold models that would need several expensive GPUs on a PC. In August 2026, Apple refreshed its desktop line with a Mac mini (M6 and M5 Pro) and a Mac Studio (M5 Max and M5 Ultra), both … [Read more...] about Best Mac for Local LLMs in 2026: Mac mini M6 & M5 Pro vs Mac Studio M5 Max & Ultra
Best Budget GPUs for Local LLMs in 2026: 16GB Cards, Used RTX 3090 & Current Prices
For local LLMs, the most important thing about a graphics card is how much VRAM it has, followed by its memory bandwidth. Raw gaming performance matters much less. That's why budget AI builders have long favoured 16GB cards and the used 24GB RTX 3090. In 2026 there's a complication: a global memory shortage has pushed GPU prices far above launch prices, especially for cards … [Read more...] about Best Budget GPUs for Local LLMs in 2026: 16GB Cards, Used RTX 3090 & Current Prices