Can you run GPT-OSS 20B on RTX 4060 Ti?
GPT-OSS 20B MXFP4 on the NVIDIA GeForce RTX 4060 Ti: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 4060 Ti runs GPT-OSS 20B. 13.8 GB weights at MXFP4 vs 14.4 GB usable VRAM; ~29 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 4060 Ti, https://modelfit.io/can-i-run/gpt-oss-20b-on-rtx-4060-ti/, 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 4060 Ti, in order of what usually breaks first.
Where the RTX 4060 Ti sits for GPT-OSS 20B
The RTX 4060 Ti is the cheapest tracked card that runs GPT-OSS 20B fully in VRAM (~29 tok/s est.). Every cheaper card in the table forces partial offload. The next step up, the RTX 3090, reaches ~73 tok/s est. with a 16k context ceiling.
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: 14.4 GB.
Workload verdicts
GPT-OSS 20B on the RTX 4060 Ti, 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 3.4 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 ~29 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 4060 Ti: FAQ
Can the NVIDIA GeForce RTX 4060 Ti run GPT-OSS 20B?
Yes. GPT-OSS 20B (MXFP4) loads in about 13.8 GB and the RTX 4060 Ti offers 14.4 GB of usable VRAM, leaving headroom for context. Expect at roughly 29 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 4060 Ti budget is 14.4 GB (16 GB x 90%).
What is the best quantization of GPT-OSS 20B for the RTX 4060 Ti?
Stick with the MXFP4 build at 13.8 GB; every heavier quant exceeds the 14.4 GB usable VRAM.
What GPU do I need to run GPT-OSS 20B comfortably?
The RTX 4060 Ti 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 4060 Ti?
None worth having: the 13.8 GB of weights alone exceed the 14.4 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is GPT-OSS 20B on the RTX 4060 Ti fast enough for coding agents?
No: the engine estimates ~29 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.