Download OpenAI’s New GPT-OSS for Free (Full 2025 Guide)

In August 2025, OpenAI quietly did something it hasn’t done in over five years — it gave the world a free, downloadable GPT.

Called GPT-OSS, this “open-weight” model comes in two sizes — a lighter 20B version that can run on laptops or cloud servers, and a 120B powerhouse for enterprise-level work. Unlike ChatGPT, GPT-OSS runs entirely on your own infrastructure, keeping your data private while letting you customise the model for your exact needs.

In this guide, you’ll learn exactly how to download GPT-OSS, set it up, and put it to work — whether you’re a developer, startup, or business leader. We’ll also cover benchmarks, monetisation opportunities, and why this release could reshape how companies use AI in 2025.

"One of the things that is unique about open models is that people can run them locally. People can run them behind their own firewall, on their own infrastructure," OpenAI co-founder Greg Brockman

An open-weight model is a large language model (LLM) whose trained parameters (“weights”) are released to the public. This allows anyone to:

• Download the model
• Run it locally or on cloud infrastructure
• Fine-tune it on their own data
• Inspect how it works under the hood
  

This contrasts with closed models like ChatGPT or Claude, where the model runs on the provider’s servers, and you access it only via API ( Application Programming Interface) sending your data to a black box.

GPT‑OSS is OpenAI’s new family of open-weight large language models, released under the Apache 2.0 license. That means you can run them locally, customize them, and use them commercially.

There are two versions:

• GPT‑OSS‑20B: Compact, efficient (3.6B active params), can run on a modern laptop with 16GB RAM.
  
  
• GPT‑OSS‑120B: A sparse Mixture of Experts model (117B total, 4 active experts), designed for high-end GPUs (80GB+ VRAM).
  
  

Unlike GPT‑4 or GPT-3.5, you don’t need to send any data to OpenAI. You can download the models and run them behind your firewall.

While GPT-4 and ChatGPT are powerful, they’re locked behind OpenAI’s servers and usage fees. GPT-OSS changes the game:

• Self-hosted — runs on your infrastructure, not OpenAI’s
• No recurring costs — pay once for hardware/cloud, no per-token charges
• Private & secure — your prompts and data never leave your system

For companies seeking a GPT-4 alternative with full control and no vendor lock-in, GPT-OSS is a strong contender.

You can download GPT-OSS directly from OpenAI’s official GitHub releases:

• GPT-OSS 20B — lighter model, runs on laptops with high VRAM or small cloud instances
• GPT-OSS 120B — enterprise-scale model for data centres or high-end GPUs

Steps:

1. Visit the official GPT-OSS repository.
2. Verify model checksum for authenticity.
3. Download model weights and tokenizer files.

(Tip: Search “download OpenAI GPT” to find official release notes and mirrors.)

Whether you want to run GPT-OSS locally or on a cloud server, the setup process is straightforward:

Local Deployment (Windows/Mac/Linux)

• Install Python and PyTorch
• Download the model weights
• Load with a framework like Hugging Face Transformers

Cloud Deployment (AWS, Azure, GCP)

• Choose a GPU instance with enough VRAM (e.g., A100, H100)
• Install required dependencies
• Deploy behind a secure API for team access

This makes GPT-OSS one of the easiest self-hosted AI models for 2025.

I’ve kept it simple and non-technical, so someone with no coding experience could follow it to install and run GPT-OSS locally.

1. Check Your Computer Specs

• For GPT-OSS 20B (medium model)
  • Works on high-end laptops/desktops
  • Example: Apple M3 Max with 64 GB RAM
  • Requires ~12–13 GB storage space
• For GPT-OSS 120B (large model)
  • Needs a desktop with a high-end NVIDIA GPU
  • Not suitable for most laptops

Tip: Start with 20B unless you have a very powerful PC or workstation.

2. Choose Your Installation Method

You have three ways to run GPT-OSS locally.
The easiest options are Ollama or LM Studio (both work on Mac and Windows).

Option A – Using Ollama (Recommended for Ease)

1. Go to Ollama’s website.
2. Download the app for Mac, Windows, or Linux.
3. Install and open the Ollama app — no terminal commands needed.
4. In the app’s dropdown menu, find the GPT-OSS models (20B or 120B).
5. Select GPT-OSS 20B for most systems.
6. Type a message — Ollama will auto-download the model the first time you run it.
7. Once downloaded, you can chat with GPT-OSS offline.

Extra: Ollama has an optional web search function (requires a free Ollama account). This feature may be slow right now because the model just launched.

Option B – Using LM Studio

1. Go to LM Studio’s website.
2. Download and install LM Studio for your OS.
3. Open LM Studio once before using its command-line installer.
4. Open Terminal (Mac) or PowerShell (Windows).
5. Paste the installation command provided on LM Studio’s download page (different for Mac/Windows).
6. Once the model downloads, open LM Studio and go to Discover → GPT-OSS.
7. Select the model and start chatting.

Option C – Technical Users

• Download GPT-OSS directly from Hugging Face.
• Requires knowledge of Python, PyTorch, and model hosting.
• Suitable for developers who want more control.

3. Using GPT-OSS on the Web (Optional)

• You can try GPT-OSS at gptosss.com without installing anything.
• Simply type in a prompt and see the output.
• Note: Web performance is slower than running locally due to heavy traffic.

4. Quick Usage Tips

• First run will be slower because the model is downloading.
• GPT-OSS can show or hide its reasoning — toggle this in the settings.
• 20B model is much faster for general use; 120B is better for complex tasks but needs powerful hardware.

"In the long term, open source will be more cost-effective... because you're not paying for the additional cost of IP and development." — Andrew Jardine, Hugging Face

Business Use Cases for GPT-OSS

• Enterprise Search — keep corporate data private while enabling AI-powered search
• Custom Chatbots — train on your company knowledge base without sending data outside
• Content Generation — blogs, reports, and internal documentation at scale
• Analytics — summarising and interpreting internal datasets securely

For business AI use cases, GPT-OSS allows deep customisation and cost savings.

Knolli lets you turn GPT-OSS into a monetisable AI co-pilot:

• Train GPT-OSS on your niche knowledge
• Offer subscription or pay-per-use access
• Embed the co-pilot on your website or share via a custom domain

Creators and companies can earn revenue by offering specialised GPT-OSS-powered tools to their audiences.

✅ Quantization (e.g., MXFP4, QLoRA) makes big models usable on smaller GPUs.

Tools to Use

• Hugging Face Transformers (Python library, hundreds of models)
  
  
• Ollama (CLI for running LLMs locally — even GPT-OSS)
  
  
• LM Studio (GUI app for chatting with local models)
  
  
• LangChain / LlamaIndex (for building RAG systems)
  
  
• vLLM / Text Generation Inference (for high-speed API hosting)

•  Not multimodal (no image/audio support)
  
  
•  No training data transparency
  
  
•  No built-in jailbreaking protection
  
  
•  No hosted version or customer support
  
  
•  Not ideal for ultra-low-latency applications
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OpenAI released GPT‑OSS without a monetization plan — no upsells, no hosted version. Why?

• A counter to China’s open-weight model dominance (DeepSeek, Qwen)
  
  
• An olive branch to governments and researchers demanding transparency
  
  
• A strategic moat: encouraging people to still use OpenAI tooling (trust, ecosystem)
  
  

Some speculate it’s also a hedge against regulation: if they release weights, they sidestep closed-model scrutiny.

You can fine-tune a model using:

GPT‑OSS changes the game. You don’t need to rent AI anymore,  you can own it.

This is the dawn of a new phase where startups, nonprofits, governments, and solo developers can:

• Deploy private copilots
  
  
• Build GPT-level AI assistants
  
  
• Train custom reasoning systems
  
  
• Avoid closed ecosystem risk
  
  

1. What is GPT‑OSS?

Open-weight AI models (20B & 120B) released by OpenAI under Apache 2.0 license.

2. What’s the difference between open-source and open-weight?

Open-weight = model weights are released.
Open-source = code and sometimes data are released too.
All open-source models are open-weight, but not vice versa.

3. Can I use GPT-OSS in my commercial product?

Yes,  OpenAI released it under Apache 2.0, which is highly permissive. You can:

• Use it commercially
• Modify and fine-tune it
• Avoid paying API fees
  

4. Which model is best for on-device apps?

• Mistral 7B (4-bit) — great performance, runs on RTX 3060
• GPT-OSS-20B — ideal for laptops w/ 16GB RAM or Apple M2 Max
• Phi-3 Mini (if released) — rumored to be small, strong, and open
  

5. Is Llama 3 open?

Meta has hinted at Llama 3 (expected 2025), likely continuing their open-weight strategy. Current top models: Llama 2 and Code Llama.

6. Can I replace ChatGPT in my company?

Yes, if:

• You’re okay with 90–95% of its quality
• You need full data control
• You want to fine-tune on internal content
  Otherwise, hybrid approaches work best.

7. Can I run it offline?

Yes — no internet, no OpenAI account needed.

8. Is it as good as GPT-4?

No — but it matches GPT-4 Mini, beats GPT-3.5 on some benchmarks, and is open.

9. Is it free to use?

Yes, you only pay for compute.

10. What hardware do I need?

• GPT‑OSS‑20B: Laptop with ~13 GB VRAM
• GPT‑OSS‑120B: Server with 80 GB GPU or multi-GPU setup

11. Is GPT-OSS free?

Yes, both the 20B and 120B versions are free to download and run.

12. How do I download GPT-OSS?

From OpenAI’s official GitHub repository (see download section above).

13. Can GPT-OSS replace GPT-4?

For many use cases, yes especially when privacy, cost, and customisation matter.

14. What are the hardware requirements for GPT-OSS?

20B model can run on a high-VRAM laptop or small cloud instance; 120B requires data centre GPUs.

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