Can I run it?
Can I run Qwythos 9B v2 on RTX 6000 Ada?
Runs great
Runs great fully on your RTX 6000 Ada at BF16, 8K context, about 29–40 tok/s. Weights 16.7 GB plus 0.27 GB of KV cache — 17.4 GB of 45.1 GB usable. Up to 512K context still fits.
Best quantBF16
Working context8K
Max that fits512K
Speed29–40 tok/s
Weights16.7 GB
KV cache0.27 GB
Total17.4 GB
Usable45.1 GB
Qwythos 9B v2 details · RTX 6000 Ada page · How this estimate is made
Run commands
llama.cpp
llama-server -hf empero-ai/Qwythos-9B-v2-GGUF:BF16 -c 8192 -ngl 32
No matching name in the Ollama library snapshot.
LM Studio
lms get empero-ai/Qwythos-9B-v2-GGUF # Then load quant BF16
Assumptions
- Estimate, not a benchmark. Decode speed uses a memory-bandwidth model at 55–75% of peak.
- KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
- BF16 size is the real GGUF file size from Hugging Face.
- 0.5 GB is reserved because this GPU also drives a display, then 5% of the rest is left free.
- Speed is quoted at 8K context, not at the maximum context that still fits.
Larger models that also fit
- Can I run Qwen3.8 27B on RTX 6000 Ada? — Runs, UD-Q8_K_L, 18–25 tok/s
- Can I run gemma 4 12b it on RTX 6000 Ada? — Runs great, BF16, 20–27 tok/s
- Can I run Qwen3.5 9B on RTX 6000 Ada? — Runs great, BF16, 29–40 tok/s
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.