RTX 5060 Ti 16GB runs GPT-OSS 20B — but there's less headroom than you'd think
Sized at Q4_K_M with a 8K context: weights, KV cache and framework overhead, against what RTX 5060 Ti 16GB leaves free. GPT-OSS 20B at Q4_K_M needs 13.8 GB and you have 16 GB available, so it fits with 2.2 GB to spare. Expect 72.1–149.8 tok/s (estimated).
What to do
GPT-OSS 20B at Q4_K_M needs 13.8 GB and you have 16 GB available, so it fits with 2.2 GB to spare.
- VRAM required at Q4_K_M and 8K context: 13.8 GB
- VRAM available on your hardware: 16 GB
Your machine is not exactly this one. Run this for your exact setup — the form opens pre-filled with RTX 5060 Ti 16GB and GPT-OSS 20B.
Questions people ask about this pairing
Can an RTX 5060 Ti 16GB run GPT-OSS 20B?
Yes. GPT-OSS 20B at Q4_K_M needs 13.8 GB and you have 16 GB available, so it fits with 2.2 GB to spare.
How fast will GPT-OSS 20B actually run?
Around 72.1–149.8 tok/s (estimated) on this setup at Q4_K_M. Decode speed on a local model is set by memory bandwidth rather than compute, so the figure moves with the card's bandwidth and the bytes read per token — not with its price.
Does a longer context change what GPT-OSS 20B needs here?
Yes, and it is the figure people forget. The 13.8 GB above already includes the KV cache at 8K; that cache grows roughly linearly with context, so doubling the window adds real gigabytes rather than a rounding error. If you plan to work with long documents on RTX 5060 Ti 16GB, size for the context you will actually use, not the default.
Related
- GPT-OSS 20B — full specs and VRAM by quantization
- NVIDIA GeForce RTX 5060 Ti 16GB — specifications and what else it runs
- Can I run GPT-OSS 20B on a NVIDIA GeForce RTX 5060 Ti 16GB?
- VRAM calculator for GPT-OSS 20B
- RTX 5080 16GB → GPT-OSS 20B: keep what you have
- RTX 5070 Ti 16GB → GPT-OSS 20B: keep what you have
Data behind this page last checked 2026-08-15.