Can I run it?
Can I run Qwen3.6 14B A3B FableVibes on GeForce RTX 3080 10GB?
Tight fit
Tight fit fully on your GeForce RTX 3080 10GB at Q4_K_M, 8K context, about 208–283 tok/s. Weights 7.88 GB plus 0.19 GB of KV cache — 8.45 GB of 9.03 GB usable. Up to 16K context still fits.
Best quantQ4_K_M
Working context8K
Max that fits16K
Speed208–283 tok/s
Weights7.88 GB
KV cache0.19 GB
Total8.45 GB
Usable9.03 GB
Qwen3.6 14B A3B FableVibes details · GeForce RTX 3080 10GB page · How this estimate is made
Run commands
llama.cpp
llama-server -hf tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF:Q4_K_M -c 8192 -ngl 40
Ollama
ollama run qwen3.6
LM Studio
lms get tvall43/Qwen3.6-14B-A3B-FableVibes-GGUF # Then load quant Q4_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%.
- Q4_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 3080 10GB? — Tight fit, UD-IQ2_S, 47–64 tok/s
- Can I run Qwen2.5 14B Instruct on GeForce RTX 3080 10GB? — Tight fit, IQ4_XS, 47–64 tok/s
- Can I run Qwen3.5 27B on GeForce RTX 3080 10GB? — Tight fit, UD-IQ2_XXS, 47–64 tok/s
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.