Can I Run GPT-OSS on NVIDIA GeForce RTX 4080 Super?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

Yes, comfortably — GPT-OSS 20B at MXFP4 needs 12.1 GB at 8K context (11.1 GB weights + 1 GB KV cache/overhead), leaving ~3.9 GB of the NVIDIA GeForce RTX 4080 Super's 16 GB, at ~191 tok/s (est.).

See what else this hardware can run →

Kup ten sprzęt NVIDIA GeForce RTX 4080 Super 16GB — 16 GB VRAM · 320 W board powerUruchom w chmurze RTX 4090 w RunPod — od $0.34/godz. · stawka sprawdzona 2026-07

lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)

Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.

Ujawnienie afiliacyjne: Niektóre odnośniki na tej stronie to linki afiliacyjne — jeśli dokonasz zakupu za ich pośrednictwem, LLM Configurator może otrzymać prowizję bez dodatkowych kosztów dla Ciebie. Jako uczestnik programu Amazon Associates, LLM Configurator zarabia na kwalifikujących się zakupach.

NVIDIA GeForce RTX 4080 Super 16GB
16 GB VRAM · 320 W board power
Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.
Sprawdź cenę na Amazon

NVIDIA GeForce RTX 4080 Super Specs

VRAM16 GB
Memory Bandwidth736 GB/s

GPT-OSS 20B on the NVIDIA GeForce RTX 4080 Super: VRAM by quantization

QuantVRAM neededFits 16 GB?Max contextWhole PC
F1642.8 GB✗ No——
Q8_023.2 GB✗ No——
Q6_K18.2 GB✗ No——
Q5_K_M15.8 GB✓ Yes8K—
Q4_K_M13.6 GB✓ Yes64K—
Q3_K_M9.9 GB✓ Yes128K—
Q2_K7.9 GB✓ Yes128K—

Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 16 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.

Context costs VRAM too. At MXFP4 on the NVIDIA GeForce RTX 4080 Super: 8K 12.1 GB · 32K 12.7 GB · 128K 15.1 GB. Every window this model publishes still fits 16 GB.

No compatible GPU? GPT-OSS 20B on 16 GB of system RAM, CPU only: runs.

GPT-OSS Sizes That Fit the NVIDIA GeForce RTX 4080 Super (at 8K context)

GPT-OSS 20BMXFP4 · 12.1 GB at 8K context · ~191 tok/s (est.)
Buy vs. rent GPT-OSS
Buy the GPU
~$999
NVIDIA GeForce RTX 4080 Super · MSRP
Rent by the hour
from $0.34/hr
RTX 4090 (24 GB) class

At 2 hrs/day, buying (~$999) beats renting at $0.34/hr after about 4.1 years.

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RunPod $0.34/hr
Rent on RunPod →
Vast.ai $0.35/hr · typical low · varies
Rent on Vast.ai →

Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.

FAQ

Can I run GPT-OSS on the NVIDIA GeForce RTX 4080 Super?

Yes, comfortably — GPT-OSS 20B at MXFP4 needs 12.1 GB at 8K context (11.1 GB weights + 1 GB KV cache/overhead), leaving ~3.9 GB of the NVIDIA GeForce RTX 4080 Super's 16 GB, at ~191 tok/s (est.).

What's the best GPT-OSS size for the NVIDIA GeForce RTX 4080 Super?

GPT-OSS 20B at MXFP4 quantization needs 12.1 GB at 8K context (11.1 GB weights + 1 GB KV cache/overhead), estimated ~191 tokens/sec.

Every GPT-OSS size on the NVIDIA GeForce RTX 4080 Super

SizeVRAM at 8KVerdictSpeed
GPT-OSS 120B71.6 GBDoes not fit—
GPT-OSS 20B12.1 GBRuns~191 tok/s

GPT-OSS on Other GPUs

Popular Models on the NVIDIA GeForce RTX 4080 Super

VRAM Tier

Troubleshooting

Buying Guide

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