Can you run GPT-OSS 20B on RTX 5080?
GPT-OSS 20B MXFP4 on the NVIDIA GeForce RTX 5080 — verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 5080 runs GPT-OSS 20B. 13.8 GB weights at MXFP4 vs 14.4 GB usable VRAM; ~79 tok/s est..
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 5080, https://modelfit.io/can-i-run/gpt-oss-20b-on-rtx-5080/, updated August 2026, CC BY 4.0.
Last updated: August 16, 2026 · Editor: ModelFit Team
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.
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.
GPT-OSS 20B on RTX 5080: FAQ
Can the NVIDIA GeForce RTX 5080 run GPT-OSS 20B?
Yes. GPT-OSS 20B (MXFP4) loads in about 13.8 GB and the RTX 5080 offers 14.4 GB of usable VRAM, leaving headroom for context. Expect at roughly 79 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 5080 budget is 14.4 GB (16 GB x 90%).
What is the best quantization of GPT-OSS 20B for the RTX 5080?
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 5080 already runs GPT-OSS 20B comfortably. Larger cards only buy you longer context or a heavier quant.