About GPT-OSS 120B
GPT-OSS 120B is OpenAI's 117B-parameter model released in August 2025, with a 128K-token context window. It is a mixture-of-experts model: all 117B parameters must sit in memory, but only ~5.1B 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 0.3 GB at an 8K context, 4.8 GB at 128K, and 4.8 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 120B need?
At MXFP4 (native) with an 8K context, GPT-OSS 120B needs about 74 GB (weights 66 GB + KV cache + overhead). The smallest common hardware that fits is a A100 80GB.
Can an RTX 4090 (24GB) run GPT-OSS 120B?
Not fully in VRAM. GPT-OSS 120B needs about 74 GB even at MXFP4 (native), so a 24 GB card would have to offload layers to system RAM at a large speed penalty.
Can a Mac run GPT-OSS 120B?
Yes — Apple Silicon with 128 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~96 GB) runs GPT-OSS 120B 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.