Member-only story
Why You Should Completely Avoid Ollama in 2026
And the way better open source options
I was an Ollama user. For a long time.
When I first started playing with local AI, Ollama was my go-to. It was simple. It was user-friendly. You typed ollama run llama3 and boom — you had a running LLM. No compiling, no configuration, no headaches. It was the "Docker of local LLMs," and that comparison wasn't a coincidence.
But starting in 2026, things changed. And not for the better.
Here’s what’s happening: whenever new people want to start using local AI, Ollama becomes the default recommendation. YouTube tutorials point to it. Blog posts assume it. Documentation treats it as the standard. And that’s a problem — because you have way better options, and continuing to use Ollama in 2026 is costing you performance, flexibility, and trust in the open-source ecosystem.
I wrote this article to save you the time I spent figuring this out the hard way. Read it. Consider switching. Your future self — and the open-source community — will thank you.
Reason 1: Ollama Is Significantly Slower Than llama.cpp
Let’s start with the most immediate problem: performance.
Multiple community benchmarks and developer reports have shown that running the same model through Ollama produces 30–70% fewer tokens per second compared to running it through llama.cpp directly. This…
