Can the RTX 6000D run gpt-oss-20b?
Yes. gpt-oss-20b fits the RTX 6000D's 84 GB at Q4_K_M (about 13.8 GB needed), running at an estimated 299 tok/s. There's room for up to FP16 if you want higher quality.
Every quantization
gpt-oss-20b (21.5B, 3.6B active) on 84 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 12 GB | 13.8 GB | Fits easily | ~299 tok/s |
| Q5_K_M | 15 GB | 16.8 GB | Fits easily | ~239 tok/s |
| Q8_0 | 23 GB | 24.8 GB | Fits easily | ~156 tok/s |
| FP16 | 43 GB | 44.8 GB | Fits easily | ~83 tok/s |
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.
Run it
Copy, paste, go
llama-server -hf unsloth/gpt-oss-20b-GGUF -ngl 99Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full gpt-oss-20b details →
Compare
Same model, other hardware - and other models on this card
gpt-oss-20b on other hardware
- RTX 5090~341 tok/s
- RTX 5090 D~341 tok/s
- RTX 4090~208 tok/s
- Radeon RX 7900 XTX~183 tok/s
- RTX 5080~183 tok/s
- RTX 3090~182 tok/s
- RTX 5080 Mobile~171 tok/s
- MacBook Pro 16" M4 Max~104 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-20b?
Yes. gpt-oss-20b fits the RTX 6000D's 84 GB at Q4_K_M (about 13.8 GB needed), running at an estimated 299 tok/s. There's room for up to FP16 if you want higher quality.
How much VRAM does gpt-oss-20b need?
About 12 GB of weights at Q4_K_M, 23 GB at Q8_0 and 43 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is gpt-oss-20b on the RTX 6000D?
Roughly 299 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 3.6B parameters are read per token, which is why it is faster than its size suggests.