Alibaba · Google · Open Source · OpenAI · Decrypt
How to Run an AI Model Locally—And Why You Might Want To
Compiled by KHAO Editorial — aggregated from 1 source. See llms.txt for citation guidance.
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Open-weight AI models now trail the best closed models by only about four months on average, and a growing number of them run on hardware you probably already own.
Key facts
- Local models gain web search and file access through MCP servers, short for Model Context Protocol, and LM Studio added support in version 0.3.17, guide
- Ollama's library lists Gemma 4 26B at 16 to 19GB and the 31B version at 19 to 20GB
- Google's Multi-Token Prediction drafters for Gemma 4, available on Ollama under Apache 2.0, speed up generation by up to 3x using speculative decoding, in which a small model proposes several tokens
- Subscriptions to paid chatbots run about $20 per month, while the software here is free
Summary
Open-weight models have trailed the best closed models by an average of four months since January 2026, according to Epoch AI's May 29 analysis. Ollama and LM Studio are free, small models need about 8GB of VRAM or Mac unified memory, and LM Studio can operate entirely offline. The largest open models, such as Alibaba's 2.4-trillion-parameter Qwen 3.8-Max, sit far beyond consumer hardware, and closed models won 24 of 31 tests in Alibaba's own comparison. "Open-weight" means the company publishes the weights, the files holding everything the model learned, so anyone can download and run them.