Can NVIDIA GeForce RTX 4080 run Qwen 2.5 14B?

14B parameter LLM model on 16GB GDDR6X

Yes — runs at 8-bit quantization
~30-37 tok/sFast
SpeedFast inference, near-native speed
QualityNear-lossless — virtually identical to FP16

VRAM Requirements

Qwen 2.5 14B is a 14B parameter model. At full precision (FP16), it requires 28GB of VRAM. Your NVIDIA GeForce RTX 4080 has 16GB, so you'll need to quantize it to 8-bit (Q8) to fit.

FP16 (Full Precision)28GB (need 12GB more)

Maximum quality, no quantization

Q8 (8-bit)14GB (2GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)9GB (7GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 16GB GDDR6X at 717 GB/s bandwidth
Recommended system RAM: 32GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

Running Qwen 2.5 14B at 8-bit quantization on NVIDIA GeForce RTX 4080 gives you virtually identical quality to full precision while using roughly half the VRAM. Most users cannot distinguish Q8 output from FP16. This is the recommended precision for daily use — it's the best balance of quality and resource usage.

How to Set It Up

Step 1: Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

Ollama is the easiest way to run local LLMs. Works on Linux, macOS, and Windows.

Step 2: Download and run Qwen 2.5 14B

ollama run qwen2.5:14b

This downloads the model (~14GB). First run takes a few minutes.

Step 3: Verify GPU is being used

nvidia-smi

Check that VRAM usage increases when the model loads. You should see ~14GB used.

NVIDIA GeForce RTX 4080 Specs

VRAM16GB GDDR6X
Memory Bandwidth717 GB/s
TDP320W
CUDA Cores9,728
Street Price~$850
AI Rating7/10

About Qwen 2.5 14B

Good balance of quality and speed. Fits on 12–16GB GPUs at Q4-Q8.

Category: LLM · Parameters: 14B · CUDA required: No (runs via llama.cpp/GGUF)