Ollama vs. Llama.cpp: The AI Open-Source Controversy

8/12/2025

Here’s the thing about the open-source world: it’s built on a delicate balance of collaboration, credit, & shared progress. When a project comes along that seems to tip that balance, things can get… heated. That’s exactly what’s been happening in the local AI scene with two huge names: Ollama & Llama.cpp.

If you’ve been paying attention, you've probably seen the whispers, the angry Reddit threads, & the YouTube videos with dramatic titles. The central question everyone seems to be asking is: Is Ollama stealing Llama.cpp's work?

Honestly, it’s a lot more complicated than a simple yes or no. This isn’t a straightforward case of code theft. It’s a messy, fascinating debate about open-source ethics, the role of venture capital, & what it means to be a good citizen in a community-driven space. So, let’s unpack this whole controversy, because it’s a big one & it says a lot about the future of AI.

First, Let’s Get on the Same Page: What Are These Two Projects?

Before we dive into the drama, it’s SUPER important to understand what Ollama & Llama.cpp actually are, because their different purposes are at the heart of this whole conflict.

Llama.cpp: The Engine of the Local AI Revolution

Imagine a powerful, stripped-down, high-performance engine. That's Llama.cpp. It's not a flashy, user-friendly application. It’s a C++ implementation of the LLaMA (and other) language models. This project, started by Georgi Gerganov, was a game-changer because it made it possible to run powerful large language models on regular consumer hardware—your MacBook, your gaming PC—instead of needing a massive, expensive server farm.

Llama.cpp is known for a few key things:

Bleeding-Edge Performance: The Llama.cpp community is obsessed with optimization. They are constantly pushing the boundaries, adopting the latest research, & squeezing every last drop of performance out of the hardware. New features & speed improvements are added at a dizzying pace.
Open & Collaborative: It’s a true open-source project. Developers from all over the world contribute, share ideas, & collectively build this incredible piece of technology. The development process is open & transparent.
A Foundation for Others: Llama.cpp isn’t just one tool; it’s the foundational layer for an entire ecosystem of other projects. Think of it like the engine that powers a hundred different car models.

Ollama: The User-Friendly Interface for the Masses

If Llama.cpp is the engine, Ollama is the beautifully designed car built around it. Ollama is a tool that takes the raw power of Llama.cpp & packages it into a simple, polished, & incredibly easy-to-use application. With a single command, you can download & run a powerful AI model on your local machine.

Ollama’s main value proposition is accessibility. It has lowered the barrier to entry for local AI, allowing people who aren't hardcore developers to experiment with models like Llama 3, Phi-3, & others. It provides a clean command-line interface & an API server, making it easy to integrate local models into other applications.

So, right off the bat, you can see the dynamic. Llama.cpp is the hard-core, community-built engine. Ollama is the user-friendly product that uses that engine. & that’s where the trouble begins.

The Heart of the Controversy: More Than Just Code

The accusation of "stealing" isn't about Ollama literally copying & pasting code and claiming it's their own. They are allowed to use Llama.cpp's code because it's under the MIT license, which is very permissive. The real issues are much more nuanced & touch on the very soul of open source.

The "Free-Riding" Accusation

The core of the frustration from the Llama.cpp community is the feeling that Ollama is "free-riding." They argue that Ollama has built its popular, VC-backed product on the back of years of hard work & innovation from a community of volunteers, without giving much back.

Here's the breakdown of that sentiment:

Lack of Contributions: For a long time, the perception was that Ollama contributed very little, if anything, back to the core Llama.cpp project. While they were benefiting from all the performance gains & new features being added by the community, they weren't actively participating in that development.
Minimal Credit: This is a huge one. For a project that owes its entire functionality to Llama.cpp, many felt that Ollama did a poor job of giving credit where credit was due. It wasn't prominently displayed on their website or in their materials. It felt, to many, like they were downplaying their reliance on Llama.cpp to make their own project look more impressive, especially to investors.
The "VC-Bait" Argument: This is where the open-source ethos clashes with the world of venture capital. Ollama has raised money from investors. This has led many in the Llama.cpp community to believe that Ollama is intentionally refraining from giving credit or contributing back in a meaningful way to make themselves look more self-sufficient & attractive as an investment ("VC-bait"). The idea is that it’s easier to get funding if you look like you built the whole car, not just the shiny body.

The Licensing Debate: Letter of the Law vs. Spirit of the Law

This is a key point. Llama.cpp uses the MIT license. Legally, the main requirement of the MIT license is that you have to include the original copyright notice in your software. For a while, there were claims that Ollama wasn't even doing this correctly, which would be a clear license violation.

But even if they are technically compliant now, the argument is about the spirit of open source, not just the letter of the license. The MIT license doesn't require you to contribute code back. It doesn't require you to give prominent credit on your homepage. It doesn't require you to financially support the upstream project.

But in the world of open source, that stuff matters. It's the unwritten social contract. If you build a successful business on a community's free work, the expectation is that you will become a good citizen of that ecosystem. You'll contribute back, you'll give credit, you'll help sustain the project you depend on. The feeling is that Ollama, for a long time, didn't do that.

The Fragmentation Problem: A Walled Garden?

This is maybe the most technical but also one of the most important parts of the controversy. The Llama.cpp community has worked hard to create open standards, like the GGUF model format, to ensure that models can be easily used across different tools & platforms. It’s all about interoperability.

Ollama, however, introduced its own way of packaging models. While you can import a GGUF model into Ollama, their primary system is different. This has led to accusations that they are creating a "lock-in" effect or a "walled garden." A model packaged for Ollama might not easily run on another Llama.cpp-based tool without some workarounds.

This goes directly against the open, collaborative spirit of the Llama.cpp project. It creates fragmentation in the ecosystem, where the goal was always to create unity & interoperability.

Ollama's Side of the Story: A Different Goal

Now, it wouldn't be a fair explanation without looking at this from Ollama's perspective. Their actions, while frustrating to the Llama.cpp community, aren't necessarily malicious. They stem from having a fundamentally different goal.

Stability Over Speed

As we mentioned, Llama.cpp is a fast-moving, sometimes-breaking project. Things can change daily. For a developer tinkering with the latest tech, that's exciting. But for a product that needs to be reliable for a broad user base, that's a nightmare.

It seems Ollama's goal is to provide a stable, reliable platform. Think of it like a "Long-Term Support" (LTS) release of a Linux distribution. It might not have the absolute newest, experimental features, but it's dependable. This is crucial if they are working with partners to have simultaneous releases of new models. Those partners need a guarantee that the platform won't break.

In fact, this has led to a significant technical shift. More recently, it’s been pointed out that Ollama is no longer using Llama.cpp as a direct library. Instead, they are linking against the lower-level library,

ggml

, which Llama.cpp itself is built on. This gives them more control over the implementation & allows them to maintain that stability, even if it means they have to "reinvent the wheel" on some of the higher-level features that Llama.cpp already provides.

Lowering the Barrier to Entry is a Good Thing

Let's be honest: Ollama has done a phenomenal job of making local AI accessible. They've brought thousands of new people into this world who would have been intimidated by the process of compiling Llama.cpp from source & figuring out all the command-line flags.

In that sense, they've grown the entire pie. More people using local AI is good for everyone, including the Llama.cpp project, even if indirectly. This is a big part of why this issue is so complex.

This push to make complex AI technology accessible is a HUGE trend right now. It's not just happening in the open-source world. You see it with companies like Arsturn, which helps businesses of all sizes create their own custom AI chatbots. Building a chatbot from scratch used to be a massive undertaking, requiring specialized developers & a ton of resources. But with a no-code platform like Arsturn, businesses can train a chatbot on their own data—their website content, their product docs, their FAQs—and have an AI assistant ready to provide instant customer support, answer questions, & engage with website visitors 24/7. It takes the complexity out of conversational AI, just like Ollama takes the complexity out of running local LLMs.

The VC in the Room: Does Funding Change Everything?

The fact that Ollama is a venture-backed company is a massive part of this story. Open-source projects are typically driven by passion & a desire to create cool stuff. When a for-profit company enters the picture, the dynamics change.

The funding brings expectations of growth, market share, & eventually, a return on that investment. This is where the "loss leader" argument comes in. Is the free, open-source Ollama tool just a way to build a massive user base that they can later monetize with a hosted, enterprise-level service? It's a classic playbook in the tech world.

This isn't inherently evil—companies need to make money. But when your product is built on the foundation of a community's free labor, it creates a lot of tension. The community feels, justifiably, that they should share in that success, at least through credit & collaboration.

For businesses looking to leverage AI, this controversy highlights the importance of understanding the ecosystem. When a business uses a tool like Arsturn to build a no-code AI chatbot, they're engaging with a business solution designed to help them generate leads & boost conversions. The value proposition is clear. With open-source, the lines can be blurrier, & the long-term sustainability of a project can be a real concern.

So, Is Ollama Actually "Stealing"?

Alright, let's get to the final verdict.

No, Ollama is not "stealing" Llama.cpp's work in a legal sense. They are using open-source software in a way that the license permits.

BUT.

They have been, at best, a poor open-source citizen for a significant period. The controversy is the direct result of them appearing to take, take, take from a community project without giving back in the ways that matter in that culture: contribution, collaboration, & credit.

The good news is that this public outcry seems to be having an effect. Ollama has started to make more of an effort to acknowledge the projects they rely on. In a recent release, they specifically thanked the GGML project. It's a small step, but it's a step in the right direction.

This whole saga is a perfect case study of the challenges & growing pains in the open-source AI world. As AI becomes more commercialized, we're going to see these kinds of conflicts again & again. It raises critical questions:

What are the responsibilities of a for-profit company that builds its business on an open-source foundation?
How do we ensure that the foundational, community-driven projects remain sustainable when so much value is being built on top of them?
Is the "spirit of open source" enough, or do we need to rethink licenses for the age of AI?

There are no easy answers here. The relationship between open-source communities & commercial entities will always be a bit messy.

Hope this was helpful in clearing up the confusion around this whole situation. It's a pretty big deal in the local AI community, & it will be interesting to see how the relationship between these two major projects evolves from here. Let me know what you think in the comments