Can the RTX 6000D run gpt-oss-120b?
Just. gpt-oss-120b at Q4_K_M needs about 71.8 GB of the RTX 6000D's 84 GB, running at an estimated 202 tok/s. Keep context short and close other GPU apps.
Every quantization
gpt-oss-120b (120.4B, 5.1B active) on 84 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 70 GB | 71.8 GB | Tight | ~202 tok/s |
| Q5_K_M | 85 GB | 86.8 GB | Too big | - |
| Q8_0 | 129 GB | 130.8 GB | Too big | - |
| FP16 | 241 GB | 242.8 GB | Too big | - |
Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 6000D's 1570 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.
Compare
Same model, other hardware - and other models on this card
gpt-oss-120b on other hardware
- DGX Spark (GB10)~35 tok/s
- RTX Spark N1X (128 GB)~35 tok/s
Also runs on the RTX 6000D
- Qwen2.5 72B~23 tok/s
- Llama 3.3 70B~23 tok/s
- Qwen3.6 35B A3B~343 tok/s
- Qwen3.5 35B A3B~343 tok/s
- dolphin-2.9.1-yi-1.5-34b~45 tok/s
- Gemma 4 31B~47 tok/s
- Qwen3 32B~47 tok/s
- GLM-4.7-Flash~347 tok/s
Frequently asked
Quick answers
Can the RTX 6000D run gpt-oss-120b?
Just. gpt-oss-120b at Q4_K_M needs about 71.8 GB of the RTX 6000D's 84 GB, running at an estimated 202 tok/s. Keep context short and close other GPU apps.
How much VRAM does gpt-oss-120b need?
About 70 GB of weights at Q4_K_M, 129 GB at Q8_0 and 241 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is gpt-oss-120b on the RTX 6000D?
Roughly 202 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 6000D's 1570 GB/s memory bandwidth; as a mixture-of-experts model only about 5.1B parameters are read per token, which is why it is faster than its size suggests.