Deploy Arcee AFM-4.5B on Arm-based Google Cloud Axion with Llama.cpp
Introduction
AFM-4.5B deployment on Google Cloud Axion with Llama.cpp
Provision a Google Cloud Axion Arm64 environment
Configure your Google Cloud Axion Arm64 environment
Build Llama.cpp on Google Cloud Axion Arm64
Install Python dependencies for Llama.cpp
Download and optimize the AFM-4.5B model for Llama.cpp
Run inference with AFM-4.5B using Llama.cpp
Benchmark and evaluate AFM-4.5B quantized models on Axion
Review your AFM-4.5B deployment on Axion
Next Steps

  Log an issue

  Fork and edit

  Discuss on Discord

About this Learning Path

Skill level:  Introductory
Reading time:  30 min
Last updated:  20 Feb 2026
Skill level:
  Introductory
Reading time:
  30 min
Last updated:
  20 Feb 2026
Author:Julien Simon
Arm IP:Neoverse
Tags: ML Google Cloud Linux Google Cloud Hugging Face Python Llama.cpp
Author:
  • Julien Simon
Arm IP:
Neoverse
Tags:
ML Google Cloud Linux Google Cloud Hugging Face Python Llama.cpp

Who is this for?

This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B small language model on Google Cloud Axion instances using Llama.cpp.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Launch an Arm-based Compute Engine instance on Google Cloud Axion
  • Build and install Llama.cpp from source
  • Download and quantize the AFM-4.5B model from Hugging Face
  • Run inference on the quantized model using Llama.cpp
  • Evaluate model quality by measuring perplexity

Prerequisites

Before starting, you will need the following:

  • A Google Cloud account with permission to launch Axion (c4a-standard-16 or larger) instances
  • Basic familiarity with Linux and SSH
Next