OpenAI's First Open-Weight Model Is Here - GPT-OSS 20B and 120B

OpenAI released GPT-OSS-20B and GPT-OSS-120B as open models under Apache 2.0

• Runs locally with Ollama LM Studio or Hugging Face
• Community feedback is split with strong offline performance but weak creativity
• Solid choice for devs and regulated industries needing local LLMs

OpenAI finally dropped two open-weight models called GPT-OSS. These aren't just demos you poke at then forget. You can run them right now on your own machine and actually get stuff done.

There are two models:

• GPT-OSS-20B. Best for high-end laptops or desktops
• GPT-OSS-120B. Needs cloud or beefy GPUs to breathe

They’re built for reasoning and code not images or sound. So think more LLaMA 3 than Gemini or GPT-4V.

Sam Altman put it this way:

“We made an open model that performs at the level of 04 Mini and runs on a high-end laptop. WTF.”

And it’s not some restricted demo. These are Apache 2.0 licensed. That means no headaches if you want to use them commercially.

This ain’t DALL·E or Sora. It can’t do vision or audio.

This puts it squarely in the classic LLM space. And that’s the point.

What’s it good at?

• Logic and code
• Works in agent setups with tools like Python or web search
• Great for debugging with visible reasoning
• You can tune how it thinks
• Fine-tuning is open

How smart is it? Pretty solid for local

GPT-OSS-20B scores better than models like Mistral 7B and LLaMA 2 13B. It even lands close to GPT-3.5 on some tests.

• 90 on MMLU (GPT-3 got 93.4)
• 80.1 on GPQA Diamond (GPT-3 had 83.3)
• 19 on Humanity's Last Exam (just behind GPT-3 but ahead of 04 Mini)

So yeah the 20B punches above its size. Strong at logic code and agent-like use.

How to run GPT-OSS locally

You’ve got three ways:

1. Ollama. Works on Mac Windows and Linux. Simple setup just run
ollama run gpt-oss

2. LM Studio. More steps but still user-friendly

3. Hugging Face. For devs who want direct control over the weights: 20 billion 120 billion

And if you just want to mess around there’s a web playground too. But it’s slow and kinda limited.

One cool feature is you can set how much the model “thinks.” OpenAI calls it “reasoning effort.” So if you want short answers or full logical chains you can pick.

Here’s an example in code:

from llama_index.llms.ollama import Ollama llm = Ollama(
model="gpt-oss:20b",
thinking=True,
reasoning_effort="high",
temperature=1.0
)

It runs fast once loaded. Around 13GB in size and works fine on machines with something like an M3 Max chip and 64GB RAM. Altman even said they’ve got a smaller version that runs on a phone.

My Test: Running GPT‑OSS‑20B on RTX 2000 Ada

I ran it locally with Ollama on Ubuntu 24.04 using a 16GB RTX 2000 Ada GPU. It worked great.

• VRAM used: ~12.2 GB
• Total GPU use: ~13.1 GB
• Room left: About 3 GB for multitasking
• GPU temps: Peaked at 65°C

No crashes no fallback. Fast replies. Clean experience.

Still the answers feel closer to GPT-4o-mini than full GPT-4o - which is understandable, just not quite enough for my current prompt engineering needs. So if you're doing prompt-heavy work it might fall short. But for local dev tasks where speed and privacy matter? It's a keeper.

How it stacks up to other local models

Here’s a quick comparison for folks working under the 16GB VRAM cap: