Qwen2.5 72B Instruct (Q5_0) — 58.2 GBon NVIDIA RTX 6000 Pro

Code Multilingual Tool Calls

Q5_0 NVIDIA RTX 6000 Pro

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 Q5_0 quantization (low quality tier), the model weighs 46.89 GB. This fits within the 96 GB of VRAM on NVIDIA RTX 6000 Pro, enabling full GPU inference.

Hardware Requirements

Model size	46.89 GB
VRAM available	96 GB
VRAM used	58.2 GB
System RAM
Min RAM required	0 GB
GPU layers	80 / 80
Context size	32,768
Backend	cuda13
Flash attention	Yes
Reading from disk	Yes

Deploy

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/q5_0/nvidia-rtx6000pro.yaml) apply

Generated `values.yaml`

/values/qwen2-5-72b-instruct/q5_0/nvidia-rtx6000pro.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-q5_0-00001-of-00013.gguf"

Start Server

llama-server \
  -m qwen2-5-72b-instruct.gguf \
  --n-gpu-layers 80 \
  --ctx-size 32768 \
  --flash-attn

Verify

curl http://localhost:8080/health

Frequently Asked Questions

How much VRAM does Qwen2.5 72B Instruct (Q5_0) need?

The Q5_0 quantization of Qwen2.5 72B Instruct requires 46.89 GB. All 80 layers fit in the 96 GB of VRAM available on NVIDIA RTX 6000 Pro, enabling full GPU acceleration.

Can I run Qwen2.5 72B Instruct on NVIDIA RTX 6000 Pro?

Yes. NVIDIA RTX 6000 Pro provides 96 GB of VRAM, which is enough to run Qwen2.5 72B Instruct (Q5_0) with all layers on the GPU for optimal performance.

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. Q5_0 compresses Qwen2.5 72B Instruct from its original size down to 46.89 GB.

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

Q5_0 is a low-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 NVIDIA RTX 6000 Pro, flash attention is enabled to maximise context length and throughput within the available 96 GB of VRAM.

How do I run Qwen2.5 72B Instruct (Q5_0) with Ollama?

Run ollama run qwen2.5:72b-instruct-q5_0 to start Qwen2.5 72B Instruct (Q5_0). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026