Can AMD Radeon PRO W7800 run Qwen 2.5 32B?

32B parameter LLM model on 32GB GDDR6 ECC

Yes — runs at 8-bit quantization
~7-9 tok/sSlow
SpeedFast inference, near-native speed
QualityNear-lossless — virtually identical to FP16
AMD GPUs lack CUDA. While Qwen 2.5 32B can technically run via llama.cpp/GGUF, the setup is more complex and less optimized than on NVIDIA hardware.

VRAM Requirements

Qwen 2.5 32B is a 32B parameter model. At full precision (FP16), it requires 64GB of VRAM. Your AMD Radeon PRO W7800 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

Running Qwen 2.5 32B at 8-bit quantization on AMD Radeon PRO W7800 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 32B

ollama run qwen2.5:32b

This downloads the model (~32GB). First run takes a few minutes.

Step 3: Verify GPU is being used

rocm-smi

Check that VRAM usage increases when the model loads. You should see ~32GB used.

AMD Radeon PRO W7800 Specs

VRAM32GB GDDR6 ECC
Memory Bandwidth576 GB/s
TDP260W
CUDA CoresN/A
Street Price~$2300
AI Rating5/10

About Qwen 2.5 32B

Strong reasoning in a more accessible size. Q4 fits on 24GB GPUs.

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