Can I run it?
Can I run Qwythos 9B v2 on GeForce RTX 4060?
Tight fit
Tight fit fully on your GeForce RTX 4060 at Q5_K_M, 8K context, about 22–30 tok/s. Weights 6.08 GB plus 0.27 GB of KV cache — 6.71 GB of 7.13 GB usable. Up to 16K context still fits.
Best quantQ5_K_M
Working context8K
Max that fits16K
Speed22–30 tok/s
Weights6.08 GB
KV cache0.27 GB
Total6.71 GB
Usable7.13 GB
Qwythos 9B v2 details · GeForce RTX 4060 page · How this estimate is made
Run commands
llama.cpp
llama-server -hf empero-ai/Qwythos-9B-v2-GGUF:Q5_K_M -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 Q5_K_M
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%.
- Q5_K_M 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 4060? — Tight fit, UD-IQ1_M, 21–29 tok/s
- Can I run gemma 4 12b it on GeForce RTX 4060? — Tight fit, IQ4_XS, 19–26 tok/s
- Can I run Qwen3.5 9B on GeForce RTX 4060? — Tight fit, UD-Q5_K_XL, 21–29 tok/s
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.