Can you run GPT-OSS 20B on RTX 4060?

GPT-OSS 20B MXFP4 on the NVIDIA GeForce RTX 4060 — verdict, VRAM math and estimated speed.

No
Quick answer

No, GPT-OSS 20B does not realistically run on the NVIDIA GeForce RTX 4060. 13.8 GB weights at MXFP4 vs 7.2 GB usable VRAM; ~9 tok/s est..

Cheapest tracked card that runs it: AMD Radeon RX 7900 XT (20 GB)

VERDICT
Does not fit
EST. SPEED
~9 tok/s
WEIGHTS
13.8 GB MXFP4

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, https://modelfit.io/can-i-run/gpt-oss-20b-on-rtx-4060/, updated August 2026, CC BY 4.0.

Last updated: August 16, 2026 · Editor: ModelFit Team

VRAM
8 GB (7.2 usable)
Model weights
13.8 GB MXFP4
Est. speed
~9 tok/s
First token
~0.9s

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.

ContextKV-cacheTotalFits
8k tokens2.0 GB15.8 GBOver
16k tokens4.0 GB17.8 GBOver
32k tokens8.0 GB21.8 GBOver
64k tokens16.0 GB29.8 GBOver
128k tokens32.0 GB45.8 GBOver

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.

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 page

GPT-OSS 20B on RTX 4060: FAQ

Can the NVIDIA GeForce RTX 4060 run GPT-OSS 20B?

Not realistically. GPT-OSS 20B (MXFP4) needs about 13.8 GB against 7.2 GB usable VRAM on the RTX 4060; even with heavy CPU offload it would run at unusable speed.

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 budget is 7.2 GB (8 GB x 90%).

What is the best quantization of GPT-OSS 20B for the RTX 4060?

Stick with the MXFP4 build at 13.8 GB — every heavier quant exceeds the 7.2 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).