Can I run it?
Can I run Qwen2.5 VL 7B Instruct on GeForce RTX 3080 10GB?
Tight fit
Tight fit fully on your GeForce RTX 3080 10GB at Q8_0, 8K context, about 49–67 tok/s. Weights 7.54 GB plus 0.44 GB of KV cache — 8.36 GB of 9.03 GB usable. Up to 16K context still fits.
Best quantQ8_0
Working context8K
Max that fits16K
Speed49–67 tok/s
Weights7.54 GB
KV cache0.44 GB
Total8.36 GB
Usable9.03 GB
Qwen2.5 VL 7B Instruct details · GeForce RTX 3080 10GB page · How this estimate is made
Run commands
llama.cpp
llama-server -hf unsloth/Qwen2.5-VL-7B-Instruct-GGUF:Q8_0 -c 8192 -ngl 28
Ollama
ollama run qwen2.5
LM Studio
lms get unsloth/Qwen2.5-VL-7B-Instruct-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 3080 10GB? — Tight fit, UD-IQ2_S, 47–64 tok/s
- Can I run gemma 4 12b it on GeForce RTX 3080 10GB? — Tight fit, Q5_K_M, 42–57 tok/s
- Can I run Qwen3.5 9B on GeForce RTX 3080 10GB? — Tight fit, UD-Q6_K_XL, 46–63 tok/s
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.