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title: I finally ditched Ollama after using llama.cpp's WebUI,...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# I finally ditched Ollama after using llama.cpp's WebUI, and I'm not going back anytime soon

**[XDA Developers](https://daily.dev/sources/xda-developers)** · 5 min read · 1 upvotes · 0 comments

## Summary

llama.cpp now ships with a built-in WebUI that launches via a single terminal command and opens in the browser, making it far more accessible than before. Compared to Ollama, llama.cpp delivers faster token generation (e.g., 100 vs 93 t/s for Gemma) and lower latency, while exposing deep inference controls like sampling parameters, context settings, and GPU offloading. The trade-off is a slightly rougher launch experience — no click-to-open icon, just a terminal command and a bookmarked localhost URL. Ollama still wins on convenience and model management for casual users, but for performance-focused or hardware-constrained users, llama.cpp's WebUI is now a compelling everyday alternative.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.xda-developers.com/i-ditched-ollama-after-using-llama-cpp-webui-and-im-not-going-back>

## Questions this post answers

### How do I launch llama.cpp's WebUI for chatting with a local model?

Open a terminal and run llama-server -m path\to\your\model.gguf --port 8080, then open a browser and navigate to localhost:8080 to access the chat interface. Unlike Ollama, there is no one-click app launch; the command needs to be run each time before the WebUI becomes accessible in the browser.

_daily.dev surfaces workflow comparisons like this for developers picking a local LLM setup._

### Is llama.cpp faster than Ollama for running local language models?

Yes, in head-to-head tests llama.cpp's WebUI generated tokens faster than Ollama on the same hardware and prompts. With Gemma, llama.cpp hit 100 tokens per second versus Ollama's 93, and started generating in about 12 seconds versus 15. With Qwen 3.5:9b, llama.cpp reached 69 t/s against Ollama's 65 t/s.

_Developers weighing local LLM tools can track speed and usability trade-offs on daily.dev._

### Should I switch from Ollama to llama.cpp's WebUI for local LLMs?

Not necessarily; Ollama still wins on convenience with one-click installs and model swaps, while llama.cpp's WebUI offers faster inference and much deeper control over sampling, context settings, and GPU offloading. Power users who want to squeeze maximum performance from their hardware benefit most from llama.cpp, while casual users may prefer Ollama's simplicity.

_daily.dev helps developers compare local AI tooling before committing to one setup._

## Similar posts on daily.dev

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- [I Switched From Ollama And LM Studio To llama.cpp And Absolutely Loving It](https://daily.dev/posts/i-switched-from-ollama-and-lm-studio-to-llama-cpp-and-absolutely-loving-it-ftqjoxmri) · It's Foss · 6 upvotes · 0 comments
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---

Tags: [#llm](https://daily.dev/tags/llm), [#self-hosting](https://daily.dev/tags/self-hosting), [#local-ai](https://daily.dev/tags/local-ai), [#ollama](https://daily.dev/tags/ollama), [#llama-cpp](https://daily.dev/tags/llama-cpp)

[View this post on daily.dev](https://daily.dev/posts/i-finally-ditched-ollama-after-using-llama-cpp-s-webui-and-i-m-not-going-back-anytime-soon-a2dcksws3)

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