GPT-OSS 20B VRAM Requirements (2026)

GPT-OSS 20B is OpenAI's open-weight release from August 2025: a 21B-parameter mixture-of-experts model with only 3.6B parameters active per token. Its memory story is unusual. It ships in native MXFP4, a 4-bit microscaling format, at about 14 GB loaded. That number is correct, but it is routinely misread. Some sizing guides report the about 14 GB as if it were a quirk or an error. It is not: MXFP4 is the native precision, not a converted GGUF quant.

TL;DR: GPT-OSS 20B loads in about 14 GB at its native MXFP4 precision. It fits a 16GB GPU (RTX 5060 Ti, 5080) with headroom, runs comfortably on 24GB (RTX 3090/4090) at ~70-87 tok/s est., and is one of the best reasons to own a 24GB Mac. There is no official GGUF to convert; the MXFP4 build is the reference.

The MXFP4 Number, Explained

Most open models are released in bf16 or fp16, then quantized by the community into GGUF files. GPT-OSS 20B was trained and released directly in MXFP4, a block-scaled 4-bit format developed for this model class. The about 14 GB figure is the official OpenAI build, distributed through Ollama as gpt-oss:20b.

This has two consequences. First, the memory requirement is fixed and small: about 14 GB is the reference, not a lossy approximation. Second, quality comparisons against Q4_K_M GGUFs of other models are not apples-to-apples. MXFP4 was designed into the training, so it loses less than a post-hoc 4-bit conversion would.

Hardware Fit Table

Engine estimates below, labeled est. The fit verdicts come from the same math that powers our hardware fit checker.

The 8GB row exists because the model technically loads with CPU offload on 8GB cards. At ~9 tok/s est. with most weights in system RAM, it is not a good experience. 16GB is the true floor.

Why 24GB Is the Sweet Spot

At 24GB, the whole model sits in fast memory with 8-10 GB left for context and the OS. This is the tier where GPT-OSS 20B makes the most sense: an RTX 3090, RTX 4090, or the M6 Mac Mini 24GB all run it as a responsive daily assistant. The MoE design means decode speed tracks the 3.6B active parameters, so even a modest 24GB card feels much faster than a dense 21B model would.

On a Mac Mini M6 24GB at ~27 tok/s est., it is one of the strongest arguments for the 24GB config over 16GB. On the M5 Pro 48GB Mac Mini at ~48 tok/s est., it is the top pick for chat, coding, and reasoning combined.

How It Compares to Alternatives at the Same Memory Budget

• Qwen 3.5 9B (~7 GB) — faster on small hardware, but a full quality tier below on reasoning and instruction following.
• Gemma 4 26B-A4B (~16 GB) — the closest MoE rival; slightly more memory, comparable speed, multimodal.
• LFM2 24B-A2B (~14 GB) — tuned for tool calling and agents, a bit faster, narrower general knowledge.

GPT-OSS 20B holds its position because about 14 GB buys you reasoning quality that used to require a 30B+ dense model.

FAQ

Is about 14 GB really the whole model?

Yes. GPT-OSS 20B ships in native MXFP4 from OpenAI. It is not a compressed community quant, and it does not need to be converted to GGUF. The about 14 GB Ollama pull is the official weights.

Can GPT-OSS 20B run on 12GB VRAM?

Not comfortably. about 14 GB of weights exceeds 12GB before context or OS overhead. An RTX 5070 or 4070 will offload part of the model to system RAM. It will work, but expect reduced speed. 16GB is the practical minimum.

What about GPT-OSS 120B?

The 120B sibling loads in about 65 GB at MXFP4 and wants a 96GB machine: an RTX PRO 6000 or a Mac Studio. See our Qwen 3.5 122B-A10B requirements guide for that memory class, which is the same tier.

Does MXFP4 quality match Q8 GGUF?

For this model, yes. Because MXFP4 was the training format, it preserves quality better than a post-hoc 8-bit GGUF conversion of a bf16 model would. There is no "upgrade path" to a higher precision build; MXFP4 is the reference.