Can I run Qwen2.5-14B-Instruct on a NVIDIA RTX 4060?
Yes — with workarounds.Workarounds
Runs with GPU + CPU offload (~38% on GPU). On a NVIDIA RTX 4060 (8 GB VRAM), Qwen2.5-14B-Instruct needs about 19 GB (Q8) and should generate at roughly ~4.0 tok/s. Recommended: Split across GPU + RAM with Q8 (offload).
Qwen/Qwen2.5-14B-InstructWhy
- Too big for your VRAM alone, but at Q8 (~19 GB) it fits across VRAM + RAM. About 38% stays on the GPU and the rest runs from RAM — slower than full-GPU, but much faster than CPU-only.
How to run Qwen2.5-14B-Instruct on a RTX 4060
Run with Ollama (easiest)
ollama run qwen2.5:14bllama.cpp with GPU offload (~38% on GPU)
# -ngl = number of layers on the GPU. Raise it until VRAM is full,
# lower it if you hit out-of-memory. -1 tries to offload everything.
llama-cli -hf Qwen/Qwen2.5-14B-Instruct -ngl 99 -p "Hello"Serve with vLLM + CPU offload
# --cpu-offload-gb moves the overflow weights to system RAM
vllm serve Qwen/Qwen2.5-14B-Instruct --cpu-offload-gb 12Python (Transformers)
from transformers import pipeline, BitsAndBytesConfig
# pip install transformers accelerate bitsandbytes
quant = BitsAndBytesConfig(load_in_8bit=True)
pipe = pipeline("text-generation", model="Qwen/Qwen2.5-14B-Instruct", device_map="auto",
model_kwargs={"quantization_config": quant})
print(pipe("Hello", max_new_tokens=50))Want it to run comfortably at higher precision? The smallest GPU that runs Qwen2.5-14B-Instruct well is the NVIDIA RTX 3080 (10GB) (10 GB).