Why some local AI users are moving from Ollama to faster tools
Ollama became popular because it made easy to run, but its core model-running work has depended heavily on llama.cpp. The main criticism is that Ollama did not clearly credit llama.cpp for a long time, then moved toward its own backend and introduced problems that llama.cpp had already solved. Reported comparisons show llama.cpp running faster than Ollama on the same hardware and model, sometimes with much higher token throughput.
Ollama’s Modelfile system can make users manage settings that are already stored inside a GGUF model file, and changing small settings may require awkward extra model entries. New model support can also lag because models often need to appear in Ollama’s registry first, and Ollama may offer fewer quantization choices than llama.cpp. llama.cpp can run GGUF models directly from , includes an , and gives more control over model settings.
Suggested alternatives include llama.cpp, llamafile, llama-swap, Jan, koboldcpp, LM Studio, Msty, and ramalama, with the practical point that faster and more controllable can matter for people building AI agents on a budget.
Key points
- Ollama is easy to start with, but critics say it is slower and less transparent than llama.cpp.
- llama.cpp may deliver higher token throughput on the same machine and model.
- Ollama’s Modelfile flow can add extra work when changing model settings.
- llama.cpp gives faster access to new GGUF models and more quantization options.
- llama-swap can help when one AI agent setup needs to load and switch between several local models.