How much VRAM does gpt-oss-120b need?

At Q4_K_M with 8K context: 71.5GB weights + 0.6GB KV cache + 1.5GB runtime overhead = 73.6GB total. RTX 4070 Ti Super offers 16.0GB usable VRAM, so the verdict is: Not feasible.

What is the best quantization for gpt-oss-120b on RTX 4070 Ti Super?

None — even Q2_K (48.4GB) exceeds this GPU's 16.0GB usable VRAM. Use a smaller model, multiple GPUs, or a cloud GPU.

Can RTX 4070 Ti Super run gpt-oss-120b?

❌ Not feasible

gpt-oss-120b @ Q4_K_M · 8K context

Needed

73.6 GB

Usable

16.0 GB

VRAM breakdown

Weights (Q4_K_M)	71.5 GB
KV cache (8K context)	0.6 GB
Runtime overhead	1.5 GB
Total	73.6 GB

Computed at 8K context with fp16 KV cache. Longer contexts need more VRAM — use the VRAM calculator for other settings.

Recommended quantization

Does not fit at any quantization

Too big for local? Rent a cloud GPU · Vast.ai →

Smaller models this GPU runs well

Phi-4 14B
Qwen3 8B

FAQ

How much VRAM does gpt-oss-120b need?: At Q4_K_M with 8K context: 71.5GB weights + 0.6GB KV cache + 1.5GB runtime overhead = 73.6GB total. RTX 4070 Ti Super offers 16.0GB usable VRAM, so the verdict is: Not feasible.
What is the best quantization for gpt-oss-120b on RTX 4070 Ti Super?: None — even Q2_K (48.4GB) exceeds this GPU's 16.0GB usable VRAM. Use a smaller model, multiple GPUs, or a cloud GPU.

Check another combination in the GPU Checker →

Data verified 2026-08-04