Can you run GPT-OSS 20B on RTX 3090?
GPT-OSS 20B MXFP4 on the NVIDIA GeForce RTX 3090: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 3090 runs GPT-OSS 20B. 13.8 GB weights at MXFP4 vs 21.6 GB usable VRAM; ~73 tok/s est. (ModelFit, 2026).
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, GPT-OSS 20B on RTX 3090, https://modelfit.io/can-i-run/gpt-oss-20b-on-rtx-3090/, updated October 2026, CC BY 4.0.
Last updated: October 3, 2026 · Editor: ModelFit Team
Get told when a better model fits your RTX 3090
Get one email when a new open-weight model is a better fit for this machine.
What limits this combo
The four constraints the fit engine checks for GPT-OSS 20B on the RTX 3090, in order of what usually breaks first.
Where the RTX 3090 sits for GPT-OSS 20B
On the RTX 3090, GPT-OSS 20B runs with room to spare (~73 tok/s est.). The cheapest card that also runs it comfortably is the RTX 4060 Ti (16 GB, ~29 tok/s est.). Context ceiling on this card: 16k; on the RTX 4060 Ti: none.
Same engine as the verdict above: 90% of VRAM usable, KV-cache at fp16, bandwidth-derived tok/s. Verdicts of the other cards are computed for GPT-OSS 20B MXFP4 exactly.
Quant explorer
Switch between the quality-gated builds tracked for this exact combo. Q4_K_M stays the default recommendation; heavier Q6/Q8 builds only appear when their exact Ollama tags are registry-verified.
Why no 2-bit builds: Q1/Q2-class quants can look attractive in a memory table, but their quality loss is large enough that ModelFit excludes them from rankings instead of inflating the catalog with junk options. Usable VRAM: 21.6 GB.
Workload verdicts
GPT-OSS 20B on the RTX 3090, graded per use case from the model's registry-verified tuning and the engine's speed estimate for this exact combo.
Grades combine tag-verified tuning (a model not built for the workload caps at C) with the tok/s floor each workload needs to feel usable. A combo that partially offloads caps at C; one that does not fit is D everywhere.
Memory math: weights + context vs budget
Weights take 13.8 GB. Context costs extra KV-cache on top; this is where long-context sessions break on cards that technically fit the weights.
The model leaves about 3.8 GB of the usable VRAM budget free after weights and 16k context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
See how fast it feels
A deterministic typing simulation for GPT-OSS 20B: first token ~0.4s (instant prefill), then ~73 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
GPT-OSS 20B on RTX 3090: FAQ
Can the NVIDIA GeForce RTX 3090 run GPT-OSS 20B?
Yes. GPT-OSS 20B (MXFP4) loads in about 13.8 GB and the RTX 3090 offers 21.6 GB of usable VRAM, leaving headroom for context. Expect at roughly 73 tokens/sec (est.).
How much VRAM does GPT-OSS 20B need?
About 13.8 GB for the weights at MXFP4, plus KV-cache for context: roughly 4.0 GB extra at 16k tokens. The RTX 3090 budget is 21.6 GB (24 GB x 90%).
What is the best quantization of GPT-OSS 20B for the RTX 3090?
Stick with the MXFP4 build at 13.8 GB; every heavier quant exceeds the 21.6 GB usable VRAM.
What GPU do I need to run GPT-OSS 20B comfortably?
The RTX 3090 already runs GPT-OSS 20B comfortably. Larger cards only buy you longer context or a heavier quant.
How much context can GPT-OSS 20B use on the RTX 3090?
Up to 16k tokens stay fully in VRAM (17.8 GB total with the KV cache). At 32k tokens the total reaches 21.8 GB and tips over the budget. Quantizing the KV cache to q8_0 roughly halves the KV column and buys back about one context tier.
Is GPT-OSS 20B on the RTX 3090 fast enough for coding agents?
Barely on paper: the engine estimates ~73 tok/s against the ~30 tok/s an agentic loop needs to feel responsive, but GPT-OSS 20B is not tuned for tool-calling, which caps its agentic grade; a coding-tuned model is the better pick here.