About GPT-OSS 20B
GPT-OSS 20B is OpenAI's 21B-parameter model released in August 2025, with a 128K-token context window. It is a mixture-of-experts model: all 21B parameters must sit in memory, but only ~3.6B are active per token, which is what makes it fast for its size. It uses a hybrid-attention design — only a fraction of its layers cache the full context, so long conversations cost far less VRAM than a classic dense model: its KV cache is about 0.2 GB at an 8K context, 3.2 GB at 128K, and 3.2 GB at the full 128K window (FP16 cache).
OpenAI ships the weights pre-quantised in MXFP4, so that is the size that matters — there is no meaningful FP16 variant to download. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see themethodology.
Frequently asked questions
How much VRAM does GPT-OSS 20B need?
At MXFP4 (native) with an 8K context, GPT-OSS 20B needs about 14 GB (weights 12 GB + KV cache + overhead). The smallest common hardware that fits is a RTX 5060 Ti 16GB.
Can an RTX 4090 (24GB) run GPT-OSS 20B?
Yes. An RTX 4090's 24 GB runs GPT-OSS 20B at MXFP4 (native) (about 14 GB at 8K context).
Can a Mac run GPT-OSS 20B?
Yes — Apple Silicon with 24 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~18 GB) runs GPT-OSS 20B at MXFP4 (native).
Related
VRAM calculator for any model ·Token counter
Last updated 2026-08-03. Architecture figures from the model's published config.json; see themethodology.