Qwen2.5 72B Instruct (Q4_0) — 19.4 GBon Hetzner GEX44

Q4_0 Hetzner GEX44

Overview

Qwen2.5 72B Instruct is a 72.71B parameter dense language model by Qwen, with code, multilingual, tool-calls capabilities. It supports a context window of up to 32,768 tokens.

Qwen2.5 72B Instruct is a 72.71-billion-parameter dense transformer from Alibaba's Qwen team, fine-tuned for instruction following, code generation, and multilingual tasks. It competes with other leading 70B instruct models while supporting 14 languages including English, Chinese, Arabic, and Japanese. The model provides native tool calling and structured output capabilities. With a 32K context window and grouped-query attention, it quantizes efficiently for self-hosted inference on high-end consumer or server-class GPU configurations.

At Q4_0 quantization (medium quality tier), the model weighs 38.51 GB. This exceeds the 20 GB of VRAM on Hetzner GEX44. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.

Hardware Requirements

Model size	38.51 GB
VRAM available	20 GB
VRAM used	19.4 GB
System RAM	64 GB
Min RAM required	20.7 GB
GPU layers	37 / 80
Context size	1,128
Backend	cuda13
Flash attention	Yes

Performance Notes

Reduced performance

43 of 80 layers will run on CPU, which may reduce inference speed.

Shorter conversations

Context size has been reduced due to memory constraints. This may affect the model's ability to handle long conversations.

Deploy

Server Local

Prerequisites

Ensure your GPU nodes are prepared with the NVIDIA container toolkit:

ansible-playbook prositronic.infra.nvidia_container_toolkit

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/qwen2-5-72b-instruct/q4_0/nvidia-rtx4000sff.yaml) apply

Generated values.yaml

/values/qwen2-5-72b-instruct/q4_0/nvidia-rtx4000sff.yaml

Loading values…

Install llama.cpp

Install llama.cpp from the official build instructions for your platform and backend.

Download Model

curl -L -o qwen2-5-72b-instruct.gguf "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct-GGUF/resolve/main/qwen2.5-72b-instruct-q4_0-00001-of-00011.gguf"

Start Server

llama-server \
  -m qwen2-5-72b-instruct.gguf \
  --n-gpu-layers 37 \
  --ctx-size 1128 \
  --flash-attn

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• Qwen2.5 72B Instruct (FP16)
• Qwen2.5 72B Instruct (Q8_0)
• Qwen2.5 72B Instruct (Q5_K_M)

Other hardware

• Qwen2.5 72B Instruct on CPU Only
• Qwen2.5 72B Instruct on NVIDIA A100 80GB
• Qwen2.5 72B Instruct on NVIDIA H100

Frequently Asked Questions

How much VRAM does Qwen2.5 72B Instruct (Q4_0) need?

The Q4_0 quantization of Qwen2.5 72B Instruct requires 38.51 GB. 37 of 80 layers fit in the 20 GB of VRAM on Hetzner GEX44; remaining layers are offloaded to CPU.

Can I run Qwen2.5 72B Instruct on Hetzner GEX44?

Yes, with reduced performance. Hetzner GEX44 can run Qwen2.5 72B Instruct (Q4_0), but only 37 of 80 layers fit in VRAM. The rest are offloaded to CPU.

What is quantization?

Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. Q4_0 compresses Qwen2.5 72B Instruct from its original size down to 38.51 GB.

What quantization should I choose for Qwen2.5 72B Instruct?

Q4_0 is a medium-quality quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.

What is flash attention and why is it enabled?

Flash attention is a memory-efficient algorithm that speeds up the attention mechanism in transformer models. It reduces VRAM usage during inference by avoiding the materialisation of the full attention matrix. For Qwen2.5 72B Instruct on Hetzner GEX44, flash attention is enabled to maximise context length and throughput within the available 20 GB of VRAM.

Why are some layers offloaded to CPU?

Hetzner GEX44 has 20 GB of VRAM, but Qwen2.5 72B Instruct (Q4_0) requires approximately 38.51 GB. Only 37 of 80 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

How do I run Qwen2.5 72B Instruct (Q4_0) with Ollama?

Run ollama run qwen2.5:72b-instruct-q4_0 to start Qwen2.5 72B Instruct (Q4_0). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026