Can I run it?
Can I run Qwythos 9B v2 on GeForce RTX 4070?
Tight fit
Tight fit fully on your GeForce RTX 4070 at Q8_0, 8K context, about 28–39 tok/s. Weights 8.87 GB plus 0.27 GB of KV cache — 9.54 GB of 10.9 GB usable. Up to 32K context still fits.
Best quantQ8_0
Working context8K
Max that fits32K
Speed28–39 tok/s
Weights8.87 GB
KV cache0.27 GB
Total9.54 GB
Usable10.9 GB
Qwythos 9B v2 details · GeForce RTX 4070 page · How this estimate is made
Run commands
llama.cpp
llama-server -hf empero-ai/Qwythos-9B-v2-GGUF:Q8_0 -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 Q8_0
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%.
- Q8_0 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 GeForce RTX 4070? — Tight fit, UD-Q2_K_XL, 27–36 tok/s
- Can I run gemma 4 12b it on GeForce RTX 4070? — Tight fit, Q6_K, 21–29 tok/s
- Can I run Qwen3.5 9B on GeForce RTX 4070? — Tight fit, Q8_0, 28–39 tok/s
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.