Qwen2.5 72B Instruct (Q8_0) — 47.5 GBon Scaleway L4-2-24G
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 Q8_0 quantization (high quality tier), the model weighs 72.27 GB. This exceeds the 48 GB of VRAM on Scaleway L4-2-24G. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.
Hardware Requirements
| Model size | 72.27 GB |
| VRAM available | 48 GB |
| VRAM used | 47.5 GB |
| System RAM | 96 GB |
| Min RAM required | 26.2 GB |
| GPU layers | 51 / 80 |
| Context size | 512 |
| Backend | cuda13 |
| Flash attention | Yes |
| Reading from disk | Yes |
Performance Notes
Deploy
Prerequisites
Ensure your GPU nodes are prepared with the NVIDIA container toolkit:
ansible-playbook prositronic.infra.nvidia_container_toolkitCommand
helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/qwen2-5-72b-instruct/q8_0/nvidia-l4-48gb.yaml) applyGenerated values.yaml
/values/qwen2-5-72b-instruct/q8_0/nvidia-l4-48gb.yaml
Loading values…Frequently Asked Questions
How much VRAM does Qwen2.5 72B Instruct (Q8_0) need?
The Q8_0 quantization of Qwen2.5 72B Instruct requires 72.27 GB. 51 of 80 layers fit in the 48 GB of VRAM on Scaleway L4-2-24G; remaining layers are offloaded to CPU.
Can I run Qwen2.5 72B Instruct on Scaleway L4-2-24G?
Yes, with reduced performance. Scaleway L4-2-24G can run Qwen2.5 72B Instruct (Q8_0), but only 51 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. Q8_0 compresses Qwen2.5 72B Instruct from its original size down to 72.27 GB.
What quantization should I choose for Qwen2.5 72B Instruct?
Q8_0 is a high-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 Scaleway L4-2-24G, flash attention is enabled to maximise context length and throughput within the available 48 GB of VRAM.
Why are some layers offloaded to CPU?
Scaleway L4-2-24G has 48 GB of VRAM, but Qwen2.5 72B Instruct (Q8_0) requires approximately 72.27 GB. Only 51 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 (Q8_0) with Ollama?
Run ollama run qwen2.5:72b-instruct-q8_0 to start Qwen2.5 72B Instruct (Q8_0). Ollama handles downloading the model weights automatically on first run.