---
title: "Running Large Language Models Privately"
url: https://daily.dev/posts/running-large-language-models-privately-4rtemlemu
source_url: https://towardsdatascience.com/running-large-language-models-privately-a-comparison-of-frameworks-models-and-costs-ac33cfe3a462
type: article
source: "Towards Data Science"
published: 2024-11-02T07:40:01.368Z
updated: 2024-12-11T15:26:01.170Z
tags: ["ai", "machine-learning", "llm", "privacy"]
reading_time: 18
upvotes: 14
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Running Large Language Models Privately

**[Towards Data Science](https://daily.dev/sources/tds)** · 18 min read · 14 upvotes · 0 comments

## Summary

Running large language models (LLMs) locally can enhance privacy and reduce dependency on external providers. Smaller models can be run on standard laptops while more powerful models need advanced hardware. The post discusses various frameworks like llama.cpp and Ollama for local deployment, focusing on speed, power consumption, and performance across different quantization levels. The conclusion highlights the cost-effectiveness and customization options of running LLMs privately compared to cloud-based solutions.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/running-large-language-models-privately-a-comparison-of-frameworks-models-and-costs-ac33cfe3a462>

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---

Tags: [#ai](https://daily.dev/tags/ai), [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#privacy](https://daily.dev/tags/privacy)

[View this post on daily.dev](https://daily.dev/posts/running-large-language-models-privately-4rtemlemu)
