A collection of 14 posts
Hands-on setup for running 70B and 120B LLMs on the Ryzen AI Max+ 395: memory allocation, ROCm vs Vulkan, quants that fit, and real tokens/sec.
Install Docker Model Runner, run your first local LLM, hit the OpenAI-compatible API, push models as OCI artifacts, and see when to switch from Ollama.
A practical, honest guide to running MiniMax M3 (428B MoE) locally: VRAM/RAM math, quant options, and the Ollama, vLLM, and LM Studio paths.
Turn your local Ollama models into a private ChatGPT with Open WebUI: one-command Docker install, document chat (RAG), voice, and safe LAN access.
DSpark is DeepSeek's open-source speculative-decoding module that makes V4-Pro and V4-Flash 51–400% faster — and it works on Qwen3 and Gemma 4 too. Here's how it works and how to use it.
Ornith 1.0 is DeepReinforce's open-source, self-scaffolding family of agentic coding models, post-trained on Qwen 3.5 and Gemma 4. This guide shows how to run each variant locally - 9B on a laptop, 35B MoE on a 24GB card, 397B on an 8-GPU box - with Ollama, LM Studio and vLLM, plus agent settings.
Ornith 1.0 is open weights you run locally; Qwen 3.7 is closed API-only. We compare benchmarks, variants, VRAM, license, and price to settle which to use for agentic and local coding in 2026.
A realistic, no-hype guide to running Qwen 3.6 27B locally as a Claude Code alternative: benchmarks vs Opus, the hardware and quant you actually need, how to wire it in, where it holds up, and what a hybrid setup actually saves you.
A 1,620-upvote 'Stop using Ollama' thread set off a real 2026 debate about switching. Here is why developers are leaving, who is overstating it, and which alternative to pick by use case: GUI, speed, production, or Apple Silicon.
Two opposite ways to run big local models: the DGX Spark's 128GB unified memory vs the RTX 5090's 1,792 GB/s bandwidth. Real benchmarks, prices, power draw, and the honest which-should-you-buy verdict for local coding in 2026.
The 2026 memory crunch reshuffled the math on local AI. Here are the cheapest viable paths to run a local LLM right now — used GPUs, used Apple Silicon, CPU+RAM for MoE models, and cloud rental — ranked by dollars, with an honest beginner verdict.
A practical 2026 guide to turning a Mac mini or Mac Studio into a headless local-LLM server: a memory-tier to model-size table, how to serve Ollama, LM Studio, and MLX to your network, realistic tokens/sec, and what to buy.
ZAYA1-8B is an Apache-2.0 MoE reasoning model with 760M active params, pretrained 100% on AMD MI300X GPUs with zero NVIDIA in the loop.
A senior-engineer walkthrough of running Cline or Continue.dev fully local against Ollama on a 24GB GPU — exact config, VRAM tables, and an honest quality reality check vs cloud Claude.