Instructions to use openai/gpt-oss-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openai/gpt-oss-20b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="openai/gpt-oss-20b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")
model = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use openai/gpt-oss-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openai/gpt-oss-20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openai/gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openai/gpt-oss-20b

SGLang

How to use openai/gpt-oss-20b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "openai/gpt-oss-20b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openai/gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "openai/gpt-oss-20b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openai/gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openai/gpt-oss-20b with Docker Model Runner:
```
docker model run hf.co/openai/gpt-oss-20b
```

NVIDIA L40S GPU's for MXFP4 quantization

#100

by lordim - opened Aug 9, 2025

Discussion

lordim

Aug 9, 2025

Are NVIDIA L40S GPU's also compatible for MXFP4 quantization? I'm trying to load gpt-oss-20b on this machine, but it seems to default to bf16.

RichardDeetlefs

Aug 10, 2025

Did it work with bf16?

entfane

Aug 10, 2025

@lordim MXFP4 is supported by Blackwell architecture, so I don't think it is compatible. At least not natively.

Gerald001

Feb 17

hi, does bf16 work with nvidia l40s?

Ivborh

Feb 27

I tried gpt 20b oss on l40s and it works perfectly with MXFP4 for me now https://devforth.io/insights/self-hosted-gpt-real-response-time-token-throughput-and-cost-on-l4-l40s-and-h100-for-gpt-oss-20b/

Not nativeley as on H100, but latency and decoding speed is very nice on Marlin still, I would say L40S is one of the good options for cu currency / throughput / cost balance.

Gerald001

Feb 27

I tried gpt 20b oss on l40s and it works perfectly with MXFP4 for me now https://devforth.io/insights/self-hosted-gpt-real-response-time-token-throughput-and-cost-on-l4-l40s-and-h100-for-gpt-oss-20b/

Not nativeley as on H100, but latency and decoding speed is very nice on Marlin still, I would say L40S is one of the good options for cu currency / throughput / cost balance.

whats the latency - can you share numbers?

Ivborh

Feb 28

I tried gpt 20b oss on l40s and it works perfectly with MXFP4 for me now https://devforth.io/insights/self-hosted-gpt-real-response-time-token-throughput-and-cost-on-l4-l40s-and-h100-for-gpt-oss-20b/

Not nativeley as on H100, but latency and decoding speed is very nice on Marlin still, I would say L40S is one of the good options for cu currency / throughput / cost balance.

whats the latency - can you share numbers?

Sure, you can take a look on charts in my link above, it is visually easier to understand, but I will give some example points: in terms of TTFT on single request without concurrency it is 2-3 seconds for sequences like 30k tokens grows to 10s on context like 70k, and 30s for longest sequences closer to context window, decoding speed is near 150token/s at very short sequences and falls down to 100 token/s for longest. Important - this uses default max num batched tokens in vLLM which is 2Ki Tokens, you can bump it higher to even improve TTFT but with risk for OOM on long/concurrent sequences, or model will not start at all.
For concurrent users, please see charts.

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