GPUs/NVIDIA GeForce RTX 5090/Qwen 2.5 Coder 32B

Can NVIDIA GeForce RTX 5090 run Qwen 2.5 Coder 32B?

32B parameter Code model on 32GB GDDR7

Yes — runs at 8-bit quantization
~34-42 tok/sFast
SpeedFast inference, near-native speed
QualityNear-lossless — virtually identical to FP16

VRAM Requirements

Qwen 2.5 Coder 32B is a 32B parameter model. At full precision (FP16), it requires 64GB of VRAM. Your NVIDIA GeForce RTX 5090 has 32GB, so you'll need to quantize it to 8-bit (Q8) to fit.

FP16 (Full Precision)64GB (need 32GB more)

Maximum quality, no quantization

Q8 (8-bit)32GB (0GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)20GB (12GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 32GB GDDR7 at 1792 GB/s bandwidth
Recommended system RAM: 64GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

Qwen 2.5 Coder 32B at this precision on NVIDIA GeForce RTX 5090 gives excellent code completion and generation. Fast enough for real-time IDE integration. Handles complex refactoring, multi-file edits, and long-context code understanding.

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 Coder 32B

ollama run qwen2.5:32b

This downloads the model (~32GB). 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 ~32GB used.

NVIDIA GeForce RTX 5090 Specs

VRAM32GB GDDR7
Memory Bandwidth1792 GB/s
TDP575W
CUDA Cores21,760
Street Price~$2800
AI Rating10/10

Other Code Models on NVIDIA GeForce RTX 5090

About Qwen 2.5 Coder 32B

Best open-source coding model. Needs 24GB+ for good quality.

Category: Code · Parameters: 32B · CUDA required: No (runs via llama.cpp/GGUF)