RUNYARD.DEV / BLOG
Guides, deep-dives, and news on local AI models, hardware, and the Runyard platform.
The short answer: 8GB gets you started, 16GB runs most models comfortably, and 24GB+ unlocks the best open-source models at full quality. Here's the full breakdown by model size and quantization.
DeepSeek Coder V2, Qwen2.5 Coder 32B, and CodeLlama 70B lead the pack — but the best choice depends entirely on your VRAM. Here's the full ranked list with benchmarks.
Ollama is faster to set up and perfect for developers. LM Studio has a polished GUI and better model discovery. The right choice depends on how you work — here's the honest comparison.
An RTX 3070, 4060, or any 8GB GPU can run surprisingly capable models. Llama 3.1 8B, Mistral 7B, DeepSeek Coder 6.7B, and more — all fit comfortably at Q4 quantization.
Meta's Llama 3.1 is one of the best open-source LLMs available. This guide walks you through every step — from picking the right model size to chatting with it in 10 minutes.
Running LLMs locally has upfront hardware costs but near-zero marginal cost. The OpenAI API has no upfront cost but charges per token. We break down exactly when local wins.
The RTX 4090 is the gold standard, but the RTX 4070 Ti offers the best value. Apple Silicon is the wildcard. Here's every major GPU ranked for LLM inference.
Runyard is a free tool that tells you exactly which AI models will run on your computer — and how well. No more guessing, no more downloading 20GB models that don't fit.
Stop downloading models that don't fit. This guide walks you through exactly how to match an open-source LLM to your GPU, RAM, and use case — then run it in minutes.
Runyard tells you which AI models run on your hardware and how fast. Here's a complete walkthrough of every feature — from entering your specs to clicking Analyze.
Everyone on YouTube is selling "Claude Code for free via OpenRouter." It's not a lie — but it's not the whole truth either. Here's what you're actually getting, and what the real free alternative looks like.