Can NVIDIA GeForce RTX 3080 10GB run Qwen 2.5 14B?

14B parameter LLM model on 10GB GDDR6X

Yes — runs at 4-bit quantization
~46-57 tok/sFast
SpeedModerate speed, usable for interactive chat
QualityGood quality with slight degradation on complex reasoning

VRAM Requirements

Qwen 2.5 14B is a 14B parameter model. At full precision (FP16), it requires 28GB of VRAM. Your NVIDIA GeForce RTX 3080 10GB has 10GB, so you'll need to quantize it to 4-bit (Q4) to fit.

FP16 (Full Precision)28GB (need 18GB more)

Maximum quality, no quantization

Q8 (8-bit)14GB (need 4GB more)

Near-lossless, ~50% size reduction

Q4 (4-bit)9GB (1GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 10GB GDDR6X at 760 GB/s bandwidth
Recommended system RAM: 32GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

At 4-bit quantization, Qwen 2.5 14B fits in NVIDIA GeForce RTX 3080 10GB's 10GB VRAM but with some quality trade-offs. Complex reasoning tasks and nuanced writing may show slight degradation. For casual chat, code assistance, and general queries, Q4 is perfectly usable. For critical work, consider a GPU with more VRAM to run at Q8.

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:q4_K_M

This downloads the Q4_K_M quantized version (~9GB). First run takes a few minutes to download.

Step 3: Verify GPU is being used

nvidia-smi

Check that VRAM usage increases when the model loads. You should see ~9GB used.

NVIDIA GeForce RTX 3080 10GB Specs

VRAM10GB GDDR6X
Memory Bandwidth760 GB/s
TDP320W
CUDA Cores8,704
Street Price~$450
AI Rating4/10

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)