What GPU do I need to run openai/gpt-oss-20b?
A 21B (MoE) reasoning model whose MXFP4 weights run within 16GB. 20.9B parameters, published in MXFP4. View on Hugging Face
What gpt-oss-20b is
gpt-oss-20b is a 21B-parameter mixture-of-experts language model published by OpenAI on Hugging Face, the smaller of two open-weight gpt-oss models aimed at lower-latency, local and specialized use cases. Its MoE weights are post-trained in MXFP4 precision, which the model card says is what lets it run within 16GB of memory. It supports configurable reasoning effort (low, medium, high), function calling, web browsing and Python code execution, and is released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from gpt-oss-20b's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Low-latency and local/on-device inference
- Reasoning and chain-of-thought tasks
- Agentic tool use and function calling
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| MXFP4 (native) | 12.8 GB | 16.0 GB | RTX 5060 Ti | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
OpenAI's own model card: MXFP4-quantized MoE weights let gpt-oss-20b run within 16GB of memory.
Cheapest way to run gpt-oss-20b at its published (MXFP4) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total).
How to run gpt-oss-20b
Run gpt-oss-20b with vLLM
From openai/gpt-oss-20b's own model card (requires vllm>=0.10.1+gptoss).
vllm serve openai/gpt-oss-20bSource: https://huggingface.co/openai/gpt-oss-20b/raw/main/README.md
Run gpt-oss-20b with Ollama
From the model card's own Ollama instructions; verified against Ollama's own library listing.
ollama run gpt-oss:20bRun gpt-oss-20b with GGUF quantizations
Prebuilt GGUF weights published at unsloth/gpt-oss-20b-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/gpt-oss-20b-GGUFDeploy gpt-oss-20b on Aquanode
Aquanode has no one-click deploy template for gpt-oss-20b; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX 5060 Ti or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More gpt-oss models
- gpt-oss-20b-BF16 (20.9B, BF16)
- Huihui-gpt-oss-20b-BF16-abliterated (20.9B, BF16)
- gpt-oss-120b (116.8B, MXFP4)
- gpt-oss-120b-Eagle3-short-context (787M, BF16)
- EAGLE3-gpt-oss-20b (350M, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
