GPUs/NVIDIA RTX 5000 Ada/Qwen 2.5 Coder 32B

Can NVIDIA RTX 5000 Ada run Qwen 2.5 Coder 32B?

32B parameter Code model on 32GB GDDR6 ECC

Yes — runs at 8-bit quantization
~11-13 tok/sSlow
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 RTX 5000 Ada 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 GDDR6 ECC at 576 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 RTX 5000 Ada 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 RTX 5000 Ada Specs

VRAM32GB GDDR6 ECC
Memory Bandwidth576 GB/s
TDP250W
CUDA Cores12,800
Street Price~$3800
AI Rating9/10

Other Code Models on NVIDIA RTX 5000 Ada

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)