How to beginner · 3 min read

How to run LLMs without GPU

Q: How to run LLMs without GPU

You can run LLMs without a GPU by using Ollama, which supports CPU-based inference. Install the ollama CLI and Python SDK, then run models locally on CPU by specifying the model and input text without GPU dependencies.

Quick answer

You can run LLMs without a GPU by using Ollama, which supports CPU-based inference. Install the ollama CLI and Python SDK, then run models locally on CPU by specifying the model and input text without GPU dependencies.

PREREQUISITES

Python 3.8+
pip install ollama
Ollama CLI installed and configured
No GPU required

Setup Ollama CLI and Python SDK

Install the Ollama CLI from the official site and set up the Python SDK to interact with local LLMs. Ollama supports CPU inference, so no GPU is needed.

bash

pip install ollama

Step by step example

Run a local LLM on CPU using Ollama's Python SDK. This example loads a model and generates text without requiring GPU acceleration.

python

import ollama

# Generate completion
response = ollama.chat(model="llama2", messages=[{"role": "user", "content": "Explain how to run LLMs without GPU."}])

print("Response:", response['choices'][0]['message']['content'])

output

Response: Running LLMs without a GPU is possible by using CPU-based inference. Ollama supports this mode, allowing you to run models locally without specialized hardware.

Common variations

Use different models supported by Ollama by changing model.
Run asynchronously with Python asyncio if supported.
Adjust prompt or parameters for temperature, max tokens, etc.

python

import asyncio
import ollama

async def async_generate():
    response = await ollama.chat(model="llama2", messages=[{"role": "user", "content": "Run LLMs without GPU."}])
    print("Async response:", response['choices'][0]['message']['content'])

asyncio.run(async_generate())

output

Async response: Running LLMs without a GPU is feasible using Ollama's CPU mode, enabling local inference without specialized hardware.

Troubleshooting

If you see slow performance, ensure your CPU has enough cores and RAM.
Check Ollama CLI is properly installed and in your PATH.
Verify model availability locally; download models if needed.
For errors, consult Ollama logs or run ollama doctor CLI command.

✅

Key Takeaways

Ollama supports running LLMs on CPU without GPU hardware.
Install Ollama CLI and Python SDK to run models locally.
Adjust model and prompt parameters for your use case.
Use asynchronous calls for improved concurrency if needed.
Troubleshoot with Ollama CLI tools and ensure system resources.

Verified 2026-04 · llama2

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