GPUFits
Can RTX 4070 Ti Super run gpt-oss-20b?
⚠️ Tight
gpt-oss-20b @ Q4_K_M · 8K context
Needed
14.7 GB
Usable
16.0 GB
VRAM breakdown
| Weights (Q4_K_M) | 12.8 GB |
| KV cache (8K context) | 0.4 GB |
| Runtime overhead | 1.5 GB |
| Total | 14.7 GB |
Computed at 8K context with fp16 KV cache. Longer contexts need more VRAM — use the VRAM calculator for other settings.
Recommended quantization
Q4_K_M
Estimated generation speed
~229 tok/s @ Q4_K_M
Theoretical estimates based on published architecture data and measured GGUF sizes; real-world speed varies ±30%.
Smaller models this GPU runs well
FAQ
- How much VRAM does gpt-oss-20b need?
- At Q4_K_M with 8K context: 12.8GB weights + 0.4GB KV cache + 1.5GB runtime overhead = 14.7GB total. RTX 4070 Ti Super offers 16.0GB usable VRAM, so the verdict is: Tight fit.
- What is the best quantization for gpt-oss-20b on RTX 4070 Ti Super?
- Q4_K_M — the highest tier that still fits within 16.0GB usable VRAM at 8K context. Lower tiers (Q3/Q2) fit too but cost noticeable quality.
- How fast does gpt-oss-20b run on RTX 4070 Ti Super?
- About 229 tokens/s at Q4_K_M (theoretical estimate, ±30% in real-world use).
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Data verified 2026-08-04