Can the RTX 6000D run Ornith-1.5-9B?
Yes. Ornith-1.5-9B fits the RTX 6000D's 84 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 150 tok/s. There's room for up to FP16 if you want higher quality.
Every quantization
Ornith-1.5-9B (9.7B) on 84 GB
| Quant | Weights | Needs | Fit | Est. speed |
|---|---|---|---|---|
| Q4_K_M | 6 GB | 7.8 GB | Fits easily | ~150 tok/s |
| Q5_K_M | 7 GB | 8.8 GB | Fits easily | ~129 tok/s |
| Q8_0 | 10 GB | 11.8 GB | Fits easily | ~90 tok/s |
| FP16 | 19 GB | 20.8 GB | Fits easily | ~47 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 ornith-ai/Ornith-1.5-9B-GGUF -ngl 99Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Ornith-1.5-9B details →
Compare
Same model, other hardware - and other models on this card
Ornith-1.5-9B on other hardware
- RTX 5090~171 tok/s
- RTX 5090 D~171 tok/s
- RTX 4090~104 tok/s
- Radeon RX 7900 XTX~92 tok/s
- RTX 5080~92 tok/s
- RTX 3090~91 tok/s
- RTX 3080 Ti~87 tok/s
- RTX 5080 Mobile~86 tok/s
Also runs on the RTX 6000D
- Llama 3.3 70B~23 tok/s
- deepseek-v4-flash-0731-spark~26 tok/s
- Qwen3.6 35B A3B~343 tok/s
- dolphin-2.9.1-yi-1.5-34b~45 tok/s
- Laguna-XS-2.1~47 tok/s
- Gemma 4 31B~47 tok/s
- OTel-2.0-LLM-31B-IT~47 tok/s
- Qwen3 32B~47 tok/s
Frequently asked
Quick answers
Can the RTX 6000D run Ornith-1.5-9B?
Yes. Ornith-1.5-9B fits the RTX 6000D's 84 GB at Q4_K_M (about 7.8 GB needed), running at an estimated 150 tok/s. There's room for up to FP16 if you want higher quality.
How much VRAM does Ornith-1.5-9B need?
About 6 GB of weights at Q4_K_M, 10 GB at Q8_0 and 19 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.
How fast is Ornith-1.5-9B on the RTX 6000D?
Roughly 150 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.