How to Set Up AMD ROCm for Local AI Inference: A Guide to PyTorch and Ollama

The era of relying exclusively on expensive cloud compute for AI is over. With the rapid evolution of open-source models like Llama 3 and Mistral, running AI locally on your own workstation is not only possible—it’s highly efficient.

For a long time, NVIDIA’s CUDA dominated the AI landscape. However, AMD’s ROCm (Radeon Open Compute) platform has matured significantly, offering a powerful, open-source alternative. When paired with massive VRAM GPUs like the AMD Radeon PRO W7900 (packing an incredible 48GB of ECC memory), you can run and fine-tune enterprise-grade models right from your desk.

In this guide brought to you by the hardware experts at Avika Retails, we’ll walk you through setting up AMD ROCm for local AI inference using PyTorch and Ollama.

What You Need to Get Started

Before diving into the software, ensure your hardware and operating system are ready:

1. An AMD GPU: You will need a compatible AMD Radeon RX 7000 series or Radeon PRO workstation card (like the W7800 or W7900).
2. Operating System: While Windows support is rapidly improving, Ubuntu 22.04 LTS remains the most stable and heavily supported environment for native ROCm AI development.
3. Updated Drivers: Ensure your system is updated with the latest AMD kernel drivers.

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Step 1: Installing AMD ROCm

First, you need to install the core ROCm stack on your Linux machine. Open your terminal and follow these steps to add the AMD repository and install the drivers.

Bash

# Update your system packages
sudo apt update && sudo apt upgrade -y

# Install the necessary prerequisites
sudo apt install wget gnupg2

# Download and add the AMD GPU installer
wget https://repo.radeon.com/amdgpu-install/6.1/ubuntu/jammy/amdgpu-install_6.1.60100-1_all.deb
sudo apt install ./amdgpu-install_6.1.60100-1_all.deb

# Install the ROCm stack and use cases
sudo amdgpu-install --usecase=rocm

Once the installation is complete, reboot your system. You can verify that your GPU is recognized by ROCm by running:

Bash

rocminfo

If your GPU details populate the screen, you are ready to move on.

Step 2: Setting Up PyTorch with ROCm Support

PyTorch is the backbone of modern AI development and machine learning. Installing the ROCm version of PyTorch allows your Python scripts to natively accelerate operations using your AMD GPU.

The easiest way to install PyTorch with ROCm is via pip. It is highly recommended to use a Python virtual environment to keep your workspace clean.

Bash

# Create and activate a virtual environment
python3 -m venv ai_env
source ai_env/bin/activate

# Install PyTorch with ROCm support (check pytorch.org for the latest version link)
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.1

Verify the Installation:

Open Python in your terminal and run a quick test to ensure PyTorch sees your AMD GPU:

Python

import torch
print(torch.cuda.is_available()) # Should return True
print(torch.cuda.get_device_name(0)) # Should return your AMD GPU name

(Note: PyTorch still uses the cuda namespace for GPU acceleration calls, but ROCm seamlessly translates these commands for AMD hardware!)

Step 3: Running Local LLMs with Ollama

If your goal is to chat with or integrate Large Language Models (LLMs) without writing complex Python scripts, Ollama is the ultimate tool. It manages model weights, formatting, and inference engines entirely in the background.

Even better, Ollama natively supports AMD ROCm out of the box!

Install Ollama:

Bash

curl -fsSL https://ollama.com/install.sh | sh

Run a Model:

Once installed, starting a local LLM is as simple as running a single command. Let’s pull and run Meta’s highly capable Llama 3 model:

Bash

ollama run llama3

Ollama will automatically download the model and begin inferencing using your AMD GPU via ROCm. You can now chat directly in your terminal, or use Ollama’s local API (running on localhost:11434) to connect the model to web interfaces like Open WebUI or custom applications.

Build Your AI Workstation with Avika Retails

Running massive models locally requires massive VRAM. Instead of paying hefty hourly fees for cloud compute, investing in an AMD Radeon PRO W7900 AI TOP 48G or a multi-GPU setup allows you to own your infrastructure securely.

At Avika Retails, we specialize in providing high-performance computer components for AI, editing, and gaming at the all-India lowest prices.

Why shop with us?

• In-Store Collection: Visit us directly at our physical store located at 103 Vishal Bhawan, 95 Nehru Place, New Delhi-110019 to pick up your hardware safely.
• Transparent Payments: We are highly reliable and responsive. Unlike other vendors, we do not charge anything extra for Bank Transfers and UPI payments. The price you see is exactly what you pay.
• Instant Support: Need a compatibility check for a multi-GPU motherboard? We are just a message away.

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