Can NVIDIA GeForce RTX 3090 Ti run Qwen 2.5 32B?
32B parameter LLM model on 24GB GDDR6X
VRAM Requirements
Qwen 2.5 32B is a 32B parameter model. At full precision (FP16), it requires 64GB of VRAM. Your NVIDIA GeForce RTX 3090 Ti has 24GB, so you'll need to quantize it to 4-bit (Q4) to fit.
Maximum quality, no quantization
Near-lossless, ~50% size reduction
Good quality, ~75% size reduction
Recommended system RAM: 48GB DDR5 (2x GPU VRAM minimum for model overflow)
What This Means in Practice
At 4-bit quantization, Qwen 2.5 32B fits in NVIDIA GeForce RTX 3090 Ti's 24GB 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 | shOllama is the easiest way to run local LLMs. Works on Linux, macOS, and Windows.
Step 2: Download and run Qwen 2.5 32B
ollama run qwen2.5:32b:q4_K_MThis downloads the Q4_K_M quantized version (~20GB). First run takes a few minutes to download.
Step 3: Verify GPU is being used
nvidia-smiCheck that VRAM usage increases when the model loads. You should see ~20GB used.
NVIDIA GeForce RTX 3090 Ti Specs
Other GPUs That Run Qwen 2.5 32B
Other LLM Models on NVIDIA GeForce RTX 3090 Ti
About Qwen 2.5 32B
Strong reasoning in a more accessible size. Q4 fits on 24GB GPUs.