Qwen2.5 72B Instruct (Q5_K_M)on CPU Only

Q5_K_M CPU Only

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_K_M quantization (medium quality tier), the model weighs 48.1 GB. This exceeds the 0 GB of VRAM on CPU Only. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.

A CPU-only configuration with no GPU acceleration. Inference runs entirely on the CPU, which is significantly slower than GPU-accelerated setups but requires no special hardware. Performance and maximum model size depend on available system RAM. Suitable for testing, development, or deployments where no GPU is available.

Hardware Requirements

Model size	48.1 GB
VRAM available	0 GB
VRAM used	0 GB
Min RAM required	48.1 GB
GPU layers	0 / 80
Context size	32,768
Backend	cpu
Flash attention	No

Performance Notes

Reduced performance

80 of 80 layers run on CPU. Your system needs at least 48.1 GB RAM. If system RAM is insufficient, data will be read from disk on demand, with significant performance impact.

Deploy

Server Local

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/qwen2-5-72b-instruct/q5_k_m/cpu.yaml) apply

Generated values.yaml

/values/qwen2-5-72b-instruct/q5_k_m/cpu.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_k_m-00001-of-00014.gguf"

Start Server

llama-server \
  -m qwen2-5-72b-instruct.gguf \
  --n-gpu-layers 0 \
  --ctx-size 32768

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 (Q4_K_M)

Other hardware

• Qwen2.5 72B Instruct on NVIDIA A100 80GB
• Qwen2.5 72B Instruct on NVIDIA H100
• Qwen2.5 72B Instruct on NVIDIA RTX 3090

Frequently Asked Questions

How much VRAM does Qwen2.5 72B Instruct (Q5_K_M) need?

The Q5_K_M quantization of Qwen2.5 72B Instruct requires 48.1 GB. The 0 GB of VRAM on CPU Only is insufficient for GPU layers, so inference runs on CPU.

Can I run Qwen2.5 72B Instruct on CPU Only?

It is possible but not recommended. CPU Only does not have enough VRAM to accelerate Qwen2.5 72B Instruct (Q5_K_M), so inference will rely on CPU and system RAM.

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_K_M compresses Qwen2.5 72B Instruct from its original size down to 48.1 GB.

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

Q5_K_M 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.

Why are some layers offloaded to CPU?

CPU Only has 0 GB of VRAM, but Qwen2.5 72B Instruct (Q5_K_M) requires approximately 48.1 GB. Only 0 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 (Q5_K_M) with Ollama?

Run ollama run qwen2.5:72b-instruct-q5_k_m to start Qwen2.5 72B Instruct (Q5_K_M). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026