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Can the NVIDIA GeForce RTX 3060 12GB run gpt-oss 20B?

70Great quality (A) · 70 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (offloads to RAM)

Runs well (offloads to RAM). At MXFP4 it needs about 13.3 GB of 12.0 GB usable, with an estimated speed of 67.9 tok/s.

Best quant
MXFP4
Generation speed
67.9 tok/s
Prompt processing
2,341 tok/s
First token (1K prompt)
0.4 s
Largest safe context
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Best app to run it on the NVIDIA GeForce RTX 3060 12GB

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Point-and-click: search, download and chat in one window.

Open gpt-oss 20B in LM Studio

  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Point-and-click: search, download and chat in one window.

Open gpt-oss 20B in LM Studio

  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.

Want an agent? gpt-oss 20B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

Reverse engineering? Serve gpt-oss 20B locally and use it from Ghidra, IDA Pro, Binary Ninja, radare2 or JADX with one of these:

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download gpt-oss 20B

Recommended for the NVIDIA GeForce RTX 3060 12GB: MXFP4. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run gpt-oss:20b

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the MXFP4 file (opens in a new tab)

huggingface.co/ggml-org/gpt-oss-20b-GGUF (opens in a new tab)

ollama run hf.co/ggml-org/gpt-oss-20b-GGUF:MXFP4

Original weights · Hugging Face

Full-precision files from OpenAI, for fine-tuning, vLLM, Transformers.

huggingface.co/openai/gpt-oss-20b (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from OpenAI. The weights themselves download from Hugging Face.

github.com/openai/gpt-oss (opens in a new tab)

git clone https://github.com/openai/gpt-oss

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 70Great quality (A) · 70 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at MXFP4, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs welloffloads to RAM
How well it runs on the NVIDIA GeForce RTX 3060 12GB: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.31.5 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score71.5 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the NVIDIA GeForce RTX 3060 12GB

Quant Memory needed Available Speed Runs Quality Download
MXFP4 13.3 GB 12.0 GB 67.9 tok/s Runs welloffloads to RAM 70Great quality (A) · 70 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)
F16 43.2 GB 12.0 GB — Won't run 70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does gpt-oss 20B need on the RTX 3060 12GB?

At MXFP4 it needs about 13.3 GB. The RTX 3060 12GB has 12.0 GB usable.

How fast is gpt-oss 20B on the RTX 3060 12GB?

About 67.9 tok/s at MXFP4, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of gpt-oss 20B should I use on the RTX 3060 12GB?

MXFP4. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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