OpenAI's gpt-oss-20b is one of the best models you can run locally, and the cheapest new 16GB card handles it — full context included. Here's how, and what to expect.
Yes — and this is one of the card's headline tricks. The RTX 5060 Ti 16GB runs [gpt-oss-20b](/can-you-run-gpt-oss-20b-locally-2026) — OpenAI's open model and one of the best things you can run at home — and it does so even at the full 128K context, thanks to OpenAI's efficient MXFP4quantization. That's genuinely impressive for a $549 card, and it makes the 5060 Ti one of the cheapest ways to run [gpt-oss](/is-gpt-oss-good-for-local-ai-2026)-20b locally. How is 16GB enough when the model can be memory-hungry at long context? Because gpt-oss-20b is a Mixture-of-Experts model in a compact 4-bit format — it fits. Here's how to set it up and what to expect.
Why 16GB is enough for this model
It comes down to how gpt-oss-20b is built. At OpenAI's native MXFP4 (4-bit) quantization, the model needs only about 12.7GB of [VRAM](/how-much-vram-do-you-need-to-run-ai-models-2026) at 8K context — comfortably inside the 5060 Ti's 16GB. And because it's a [Mixture-of-Experts](/moe-vs-dense-ai-models-explained-2026) model (only a few billion parameters active per token), it runs fast for a 20B-class model. The one thing to watch is context length: gpt-oss-20b supports up to 128K [context](/what-is-a-context-window-explained-2026), and the KV cache grows as you fill it, so very long context pushes toward the 16GB limit. The good news — and the card's standout claim — is that the 5060 Ti can still run it at full 128K context in practice, which many pricier setups struggle to do cheaply. For everyday use (8K-ish context), you'll have comfortable headroom; for maximum-context work, you're near the edge but it works. Either way, a $549 card running an OpenAI model at full context is a genuinely strong story.
gpt-oss-20b is ~12.7GB at 8K context and a fast MoE model — so the 5060 Ti's 16GB fits it, even at 128K. · Unsplash
How to run it (and when to want more)
Setup is simple: install [Ollama or LM Studio](/ollama-vs-lm-studio-which-should-you-use-2026), pull gpt-oss-20b (they'll grab the MXFP4 build), and start chatting — it runs on the 5060 Ti out of the box. For the smoothest experience, keep your context reasonable (8K is plenty for most tasks) and you'll have headroom to spare; when you need long context (whole documents, long histories), the card can still do it at up to 128K, just with less margin. If you find yourself constantly running gpt-oss-20b at very long context and wanting to run other big models, that's the signal to consider [24GB](/rtx-5060-ti-16gb-vs-used-rtx-3090-local-ai-2026) (a used RTX 3090) for more comfort — see our best GPU for gpt-oss-20b pick. But for most people, the answer is happy: the cheapest new 16GB card runs one of the best open models at home, full context included. It's exactly the kind of thing that makes the 5060 Ti such good value for local AI.
Quick answers
Can a 16GB GPU run gpt-oss-20b?
Yes. At OpenAI's native MXFP4 (4-bit) quantization, gpt-oss-20b needs about 12.7GB of VRAM at an 8K context window, which fits comfortably in a 16GB card like the RTX 5060 Ti. Because it's a Mixture-of-Experts model with few active parameters per token, it also runs fast for a 20B-class model. The one thing to watch is context length: gpt-oss-20b supports up to 128K context, and the KV cache grows as you fill it, pushing toward the 16GB limit at maximum context. In practice the RTX 5060 Ti 16GB can run it even at full 128K context, making a 16GB card a genuinely capable and cheap way to run this model.
Is the RTX 5060 Ti 16GB enough for gpt-oss-20b at full context?
Yes — it's one of the card's standout capabilities. The RTX 5060 Ti 16GB can run gpt-oss-20b at its full 128K context window thanks to the efficient MXFP4 quantization, which many pricier setups struggle to do affordably. At normal 8K context the model uses about 12.7GB, leaving comfortable headroom; at maximum context you're closer to the 16GB edge but it still works. If you plan to run very long context constantly and also want to run other large models, a 24GB card gives more breathing room, but for running gpt-oss-20b specifically, the $549 RTX 5060 Ti is enough, full context included.
What is the cheapest GPU to run gpt-oss-20b?
Among new cards, the RTX 5060 Ti 16GB (around $549) is one of the cheapest capable options — it runs gpt-oss-20b at OpenAI's MXFP4 quantization, even at full 128K context. On the used market, a used RTX 3090 (24GB) can be a similar or somewhat higher price but adds more VRAM and speed, giving extra context headroom. If you want a new card with a warranty and low power, the RTX 5060 Ti 16GB is the value pick for gpt-oss-20b; if you want more headroom and don't mind buying used, the RTX 3090 is the alternative. Both are far cheaper than the hardware the 120B version needs.
