Can you run GPT-OSS 20B on RTX 3060?
GPT-OSS 20B MXFP4 on the NVIDIA GeForce RTX 3060: verdict, VRAM math and estimated speed.
Yes, but slowly: GPT-OSS 20B spills past the RTX 3060. 13.8 GB weights at MXFP4 vs 10.8 GB usable VRAM; ~26 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 3060, https://modelfit.io/can-i-run/gpt-oss-20b-on-rtx-3060/, updated October 2026, CC BY 4.0.
Last updated: October 3, 2026 · Editor: ModelFit Team
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What limits this combo
The four constraints the fit engine checks for GPT-OSS 20B on the RTX 3060, in order of what usually breaks first.
Where the RTX 3060 sits for GPT-OSS 20B
The RTX 3060 is the tightest way to run GPT-OSS 20B: it loads with partial offload at ~26 tok/s est.. The cheapest card that runs it without spilling into system RAM is the RTX 4060 Ti (16 GB, ~29 tok/s est.).
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: 10.8 GB.
Workload verdicts
GPT-OSS 20B on the RTX 3060, 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.
This configuration exceeds the comfortable VRAM budget by about 7.0 GB. Expect offload pressure or a shorter safe 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 ~0.5s (instant prefill), then ~26 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Upgrade path
The cheapest tracked card that runs GPT-OSS 20B comfortably is the AMD Radeon RX 7900 XT (20 GB VRAM).
See the RX 7900 XT pageGPT-OSS 20B on RTX 3060: FAQ
Can the NVIDIA GeForce RTX 3060 run GPT-OSS 20B?
Barely. GPT-OSS 20B (MXFP4) needs about 13.8 GB but the RTX 3060 has 10.8 GB usable VRAM, so part of the model spills to system RAM and speed drops to at roughly 26 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 3060 budget is 10.8 GB (12 GB x 90%).
What is the best quantization of GPT-OSS 20B for the RTX 3060?
Stick with the MXFP4 build at 13.8 GB; every heavier quant exceeds the 10.8 GB usable VRAM.
What GPU do I need to run GPT-OSS 20B comfortably?
The cheapest tracked card that runs GPT-OSS 20B (MXFP4) fully in VRAM is the AMD Radeon RX 7900 XT (20 GB).
How much context can GPT-OSS 20B use on the RTX 3060?
None worth having: the 13.8 GB of weights alone exceed the 10.8 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is GPT-OSS 20B on the RTX 3060 fast enough for coding agents?
No: the engine estimates ~26 tok/s against the ~30 tok/s an agentic loop needs to feel responsive. Chat will feel fine, but multi-step agent runs will drag; a smaller model or a bigger card is the fix.