Can I run it?
Can I run Qwen2.5 14B Instruct on GeForce RTX 5090?
Tight fit
Tight fit fully on your GeForce RTX 5090 at F16, 4K context, about 32–44 tok/s. Weights 27.5 GB plus 0.75 GB of KV cache — 28.8 GB of 29.9 GB usable.
Best quantF16
Working context4K
Max that fits4K
Speed32–44 tok/s
Weights27.5 GB
KV cache0.75 GB
Total28.8 GB
Usable29.9 GB
Qwen2.5 14B Instruct details · GeForce RTX 5090 page · How this estimate is made
Run commands
llama.cpp
llama-server -hf bartowski/Qwen2.5-14B-Instruct-GGUF:F16 -c 4096 -ngl 48
Ollama
ollama run qwen2.5
LM Studio
lms get bartowski/Qwen2.5-14B-Instruct-GGUF # Then load quant F16
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%.
- F16 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 4K context, not at the maximum context that still fits.
Larger models that also fit
- Can I run Qwen3.8 27B on GeForce RTX 5090? — Tight fit, UD-Q8_K_L, 34–47 tok/s
- Can I run Qwen3.6 35B A3B on GeForce RTX 5090? — Tight fit, UD-Q6_K, 364–497 tok/s
- Can I run Qwen3.8 35B A3B Distill on GeForce RTX 5090? — Tight fit, Q6_K, 374–510 tok/s
Updated 2026-10-17. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.