Can an RTX 3080 10GB run GPT OSS 20B?
No
Only barely. It loads at IQ3_XXS, which compresses the weights far enough to visibly degrade the model, so the honest answer is that this pairing does not work.
The numbers
GPT OSS 20B has 20.91 billion parameters across 24 layers, of which 4.18 billion are read for each token. The GeForce RTX 3080 10GB has 10 GB of VRAM at 760 GB/s, of which about 9.2 GB is left for a model once the operating system has taken its share.
| Quantisation | Total needed | Fits | Speed |
|---|---|---|---|
| Q8_0 | 21.47 GB | no | — |
| Q6_K | 16.75 GB | no | — |
| Q5_K_M | 14.63 GB | no | — |
| Q4_K_M | 12.54 GB | no | — |
| IQ4_XS | 11.13 GB | no | — |
| Q3_K_M | 10.3 GB | no | — |
| IQ3_XXS degraded | 8.23 GB | yes | 416.9 t/s |
| Q2_K degraded | 8.94 GB | yes | 380.9 t/s |
| IQ2_XXS degraded | 5.8 GB | yes | 618.1 t/s |
3 of the 9 levels fit. Levels marked degraded are listed for completeness, not as advice.
What to do instead
Shortening the context window will not rescue this one: at 4k it still wants 12.45 GB against 12.54 GB at 8k, because the weights rather than the cache are what fill the card. The gap here is too large for settings to close.
Or the hardware that does run it
The cheapest device we track that runs GPT OSS 20B at a quantisation worth using is the GeForce RTX 2060 12GB, around $160 used , running it at IQ4_XS at about 132.8 tokens per second. Check the current price (affiliate link; indicative price reviewed 2026-09-01). Compare every option.
See how fast it feels
GeForce RTX 3080 10GB running GPT OSS 20B at IQ3_XXS
YouWhy does my model use more memory when the conversation gets longer?
Because of the KV cache. Every token you send leaves behind a key and a value vector in each layer of the model, and those stay in memory for as long as the conversation lasts.
The weights are a fixed cost: load a 4-bit 8B model and that is about 4.8 GB, whether you write one word or ten thousand. The cache is the part that grows, and it grows in a straight line with the number of tokens in the window.
How steeply depends on the model's attention design. With grouped-query attention, several query heads share one key-value pair, which cuts the cache by that ratio. Without it, every head keeps its own, and a long context can cost more memory than the weights themselves.
If you are short on memory, the first thing to try is lowering the context window in your runtime, not the quantisation.
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU. The same answer on RTX 4090 at Q8_0 would take about 901 ms.
Keep going
Everything the GeForce RTX 3080 10GB runs, the full breakdown for GPT OSS 20B, or check a different pairing in the calculator.