How to run gpt-oss locally with Ollama

Want to get OpenAI gpt-oss running on your own hardware? This guide will walk you through how to use Ollama to set up gpt-oss-20b or gpt-oss-120b locally, to chat with it offline, use it through an API, and even connect it to the Agents SDK.

Note that this guide is meant for consumer hardware, like running a model on a PC or Mac. For server applications with dedicated GPUs like NVIDIA’s H100s, check out our vLLM guide.

Pick your model

Ollama supports both model sizes of gpt-oss:

gpt-oss-20b
- The smaller model
- Best with ≥16GB VRAM or unified memory
- Perfect for higher-end consumer GPUs or Apple Silicon Macs
gpt-oss-120b
- Our larger full-sized model
- Best with ≥60GB VRAM or unified memory
- Ideal for multi-GPU or beefy workstation setup

A couple of notes:

These models ship MXFP4 quantized out the box and there is currently no other quantization
You can offload to CPU if you’re short on VRAM, but expect it to run slower.

Quick setup

Install Ollama → Get it here
Pull the model you want:

# For 20B
ollama pull gpt-oss:20b

# For 120B
ollama pull gpt-oss:120b

Chat with gpt-oss

Ready to talk to the model? You can fire up a chat in the app or the terminal:

ollama run gpt-oss:20b

Ollama applies a chat template out of the box that mimics the OpenAI harmony format. Type your message and start the conversation.

Use the API

Ollama exposes a Chat Completions-compatible API, so you can use the OpenAI SDK without changing much. Here’s a Python example:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",  # Local Ollama API
    api_key="ollama"                       # Dummy key
)

response = client.chat.completions.create(
    model="gpt-oss:20b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain what MXFP4 quantization is."}
    ]
)

print(response.choices[0].message.content)

If you’ve used the OpenAI SDK before, this will feel instantly familiar.

Alternatively, you can use the Ollama SDKs in Python or JavaScript directly.

Using tools (function calling)

Ollama can:

Call functions
Use a built-in browser tool (in the app)

Example of invoking a function via Chat Completions:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather in a given city",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"]
            },
        },
    }
]

response = client.chat.completions.create(
    model="gpt-oss:20b",
    messages=[{"role": "user", "content": "What's the weather in Berlin right now?"}],
    tools=tools
)

print(response.choices[0].message)

Since the models can perform tool calling as part of the chain-of-thought (CoT) it’s important for you to return the reasoning returned by the API back into a subsequent call to a tool call where you provide the answer until the model reaches a final answer.

Responses API workarounds

Ollama doesn’t (yet) support the Responses API natively.

If you do want to use the Responses API you can use Hugging Face’s Responses.js proxy to convert Chat Completions to Responses API.

For basic use cases you can also run our example Python server with Ollama as the backend. This server is a basic example server and does not have the

pip install gpt-oss
python -m gpt_oss.responses_api.serve \
    --inference_backend=ollama \
    --checkpoint gpt-oss:20b

Agents SDK integration

Want to use gpt-oss with OpenAI’s Agents SDK?

Both Agents SDK enable you to override the OpenAI base client to point to Ollama using Chat Completions or your Responses.js proxy for your local models. Alternatively, you can use the built-in functionality to point the Agents SDK against third party models.

Python: Use LiteLLM to proxy to Ollama through LiteLLM
TypeScript: Use AI SDK with the ollama adapter

Here’s a Python Agents SDK example using LiteLLM:

import asyncio
from agents import Agent, Runner, function_tool, set_tracing_disabled
from agents.extensions.models.litellm_model import LitellmModel

set_tracing_disabled(True)

@function_tool
def get_weather(city: str):
    print(f"[debug] getting weather for {city}")
    return f"The weather in {city} is sunny."


async def main(model: str, api_key: str):
    agent = Agent(
        name="Assistant",
        instructions="You only respond in haikus.",
        model=LitellmModel(model="ollama/gpt-oss:120b", api_key=api_key),
        tools=[get_weather],
    )

    result = await Runner.run(agent, "What's the weather in Tokyo?")
    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())

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