GPUs/NVIDIA H100 80GB/Qwen 2.5 14B

Can NVIDIA H100 80GB run Qwen 2.5 14B?

14B parameter LLM model on 80GB HBM3

Yes — runs at full precision
~75-93 tok/sExcellent
SpeedFastest possible inference
QualityMaximum quality, no degradation

VRAM Requirements

Qwen 2.5 14B is a 14B parameter model. At full precision (FP16), it requires 28GB of VRAM. Your NVIDIA H100 80GB has 80GB — enough to run it without any quantization.

FP16 (Full Precision)28GB (52GB free)

Maximum quality, no quantization

Q8 (8-bit)14GB (66GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)9GB (71GB 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

With NVIDIA H100 80GB running Qwen 2.5 14B at full precision, you get the highest quality responses with no quantization artifacts. This is ideal for tasks requiring nuanced reasoning, creative writing, and complex analysis. You'll have the best possible experience with this model.

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 14B

ollama run qwen2.5:14b

This downloads the model (~28GB). 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 ~28GB used.

NVIDIA H100 80GB Specs

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

About Qwen 2.5 14B

Good balance of quality and speed. Fits on 12–16GB GPUs at Q4-Q8.

Category: LLM · Parameters: 14B · CUDA required: No (runs via llama.cpp/GGUF)