GPUs/NVIDIA H100 80GB/Qwen 2.5 Coder 32B

Can NVIDIA H100 80GB run Qwen 2.5 Coder 32B?

32B parameter Code model on 80GB HBM3

Yes — runs at full precision
~33-41 tok/sFast
SpeedFastest possible inference
QualityMaximum quality, no degradation

VRAM Requirements

Qwen 2.5 Coder 32B is a 32B parameter model. At full precision (FP16), it requires 64GB of VRAM. Your NVIDIA H100 80GB has 80GB — enough to run it without any quantization.

FP16 (Full Precision)64GB (16GB free)

Maximum quality, no quantization

Q8 (8-bit)32GB (48GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)20GB (60GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 80GB HBM3 at 3350 GB/s bandwidth
Recommended system RAM: 160GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

Qwen 2.5 Coder 32B at this precision on NVIDIA H100 80GB 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 (~64GB). 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 ~64GB used.

NVIDIA H100 80GB Specs

VRAM80GB HBM3
Memory Bandwidth3350 GB/s
TDP350W
CUDA Cores14,592
Street Price~$22000
AI Rating10/10

Other Code Models on NVIDIA H100 80GB

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)