Source URL: https://www.theregister.com/2025/08/07/run_openai_gpt_oss_locally/
Source: The Register
Title: How to run OpenAI’s new gpt-oss-20b LLM on your computer
Feedly Summary: All you need is 24GB of RAM, and unless you have a GPU with its own VRAM quite a lot of patience
Hands On Earlier this week, OpenAI released two popular open-weight models, both named gpt-oss. Because you can download them, you can run them locally.…
AI Summary and Description: Yes
Summary: The text discusses the recent release of two open-weight models by OpenAI, emphasizing the hardware requirements for running these models locally. This information is significant for AI professionals, particularly those involved in generative AI and LLM (Large Language Model) applications, as it sheds light on the accessibility and infrastructure needs for deploying such models.
Detailed Description: The text highlights the current capabilities provided by OpenAI with its release of open-weight models and outlines the necessary hardware specifications for running these models effectively. Key points include:
– **Hardware Requirements**: The mention of needing 24GB of RAM and a GPU with adequate VRAM indicates the computational demands associated with running advanced AI models, which is pertinent for organizations planning to deploy such technologies.
– **Open-Weight Models**: The release of the “gpt-oss” models makes them available for local execution, thereby decreasing dependencies on cloud infrastructure, which could empower developers and researchers to experiment more freely and develop customized solutions without the constraints typically associated with proprietary models.
– **Infrastructure Considerations**: The hardware specifications are crucial for planning infrastructure investments, as organizations must ensure they have the appropriate computational resources.
Overall, this information is valuable for professionals in AI development and deployment, as it also opens discussions about the implications of running advanced models on local systems—especially related to system security, inference efficiency, and the potential risks associated with model deployment.