Why It's Time to Move On from Ollama for Local LLMs

Ollama, once a pioneer in making local LLMs accessible, has strayed from its original mission. Initially, it simplified the use of llama.cpp, a C++ inference engine by Georgi Gerganov, which was pivotal in running LLaMA models on consumer laptops. However, Ollama has been criticized for not crediting llama.cpp and failing to comply with open-source licenses. Despite community pressure, Ollama only minimally acknowledged its reliance on llama.cpp.

In 2025, Ollama attempted to distance itself from llama.cpp by developing a custom backend using ggml, but this move reintroduced previously solved bugs, resulting in inferior performance. Benchmarks showed llama.cpp running significantly faster than Ollama. Additionally, Ollama misled users by misnaming models, causing confusion and reputational damage to model creators like DeepSeek.

Ollama's release of a closed-source app contradicted its open-source roots, raising community concerns. The introduction of the Modelfile, a separate configuration file, complicated what GGUF had simplified, forcing users to deal with unnecessary complexities. Ollama's registry bottleneck delayed access to new models, and its cloud pivot raised privacy concerns, as prompts could be routed to third-party providers without clear data retention policies.

The venture capital-backed company followed a familiar pattern: build on open-source, minimize attribution, create lock-in, and introduce monetization through cloud services. Ollama's proprietary model registry format further entrenched users, making it difficult to switch to other tools.

Alternatives like llama.cpp, llama-swap, LM Studio, and others offer more transparent, efficient, and community-driven solutions without the drawbacks of Ollama. The local LLM ecosystem thrives on open-source contributions, and tools like llama.cpp are essential for its continued accessibility and innovation.