For most users that wanted to run LLM locally, ollama solved the UX problem.
One command, and you are running the models even with the rocm drivers without knowing.
If llama provides such UX, they failed terrible at communicating that. Starting with the name. Llama.cpp: that's a cpp library! Ollama is the wrapper. That's the mental model. I don't want to build my own program! I just want to have fun :-P
Having read above article, I just gave llama.cpp a shot. It is as easy as the author says now, though definitely not documented quite as well. My quickstart:
Go to localhost:8000 for the Web UI. On Linux it accelerates correctly on my AMD GPU, which Ollama failed to do, though of course everyone's mileage seems to vary on this.
Was hoping it was so easy :) But I probably need to look into it some more.
llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma4'
llama_model_load_from_file_impl: failed to load model
This is correct, and I avoided it for this reason, did not have the bandwidth to get into any cpp rabbit hole so just used whatever seemed to abstract it away.
Notwithstanding the fact that there's about zero difference between `ollama run model-name` and `llama-cpp -hf model-name`, and that running things in the terminal is already a gigantic UX blocker (Ollama's popularity comes from the fact that it has a GUI), why are you putting the blame back on an open source project that owes you approximately zero communication ?
No mention of the fact that Ollama is about 1000x easier to use. Llama.cpp is a great project, but it's also one of the least user friendly pieces of software I've used. I don't think anyone in the project cares about normal users.
I started with Ollama, and it was great. But I moved to llama.cpp to have more up-to-date fixes. I still use Ollama to pull and list my models because it's so easy. I then built my own set of scripts to populate a separate cache directory of hardlinks so llama-swap can load the gguf's into llama.cpp.
Exactly. The blog post states that the alternatives listed are similarly intuitive. They are not. If you just need a chat app, then sure, there’s plenty of options. But if you want an OpenAI compatible API with model management, accessibility breaks down fast.
I’m open to suggestions, but the alternatives outlined in the blog post ain’t it.
The reported alternatives seem pretty User-Friendly to me:
> LM Studio gives you a GUI if that’s what you want. It uses llama.cpp under the hood, exposes all the knobs, and supports any GGUF model without lock-in.
> Jan(https://www.jan.ai/) is another open-source desktop app with a clean chat interface and local-first design.
> Msty(https://msty.ai/) offers a polished GUI with multi-model support and built-in RAG. koboldcpp is another option with a web UI and extensive configuration options.
API wise: LM Studio has REST, OpenAI and more API Compatibilities.
Just in case you haven't seen it yet, llama.cpp now has a router mode that lets you hot-swap models. I've switched over from llama-swap and have been happy with it.
I spend like 2 hours trying to get vulkan acceleration working with ollama, no luck (half models are not supported and crash it). With llama.cpp podman container starts and works in 5 minutes.
Thanks for writing this, I hope people here will actually read this and not assume this is some unfounded hit piece. I was involved a little bit in llama.cpp and knew most of what you wrote and it’s just disgusting how ollama founders behaved!
For people looking for alternatives, I would also recommend llama-file, it’s a one file executable for any OS that includes your chosen model: https://github.com/mozilla-ai/llamafile?tab=readme-ov-file
It’s truly open source, backed by Mozilla, openly uses llama.cpp and was created by wizard Justine Tunney of CosmopolitanC fame.
> Red Hat’s ramalama is worth a look too, a container-native model runner that explicitly credits its upstream dependencies front and center. Exactly what Ollama should have done from the start.
% ramalama run qwen3.5-9b
Error: Manifest for qwen3.5-9b:latest was not found in the Ollama registry
Do they still not let you change the default model folder? You had to go through this whole song and dance to manually register a model via a pointless dockerfile wannabe that then seemed to copy the original model into their hash storage (again, unable to change where that storage lived).
At the time I dropped it for LMStudio, which to be fair was not fully open source either, but at least exposed the model folder and integrated with HF rather than a proprietary model garden for no good reason.
This also annoyed me a lot. I was running it before upgrading the SSD storage and I wanted to compare with LM Studio. Figured it would be good to have both interfaces use the same models downloaded from HF.
Had to go down the same rabbit hole of finding where things are, how they're sorted/separated/etc. It was unnecessarily painful
With such concurrency in the market, it is unforgivable to manage a product that way. The concurrency will kill you.
Clients get disappointed, alternatives have better services, and more are popping out monthly. If they continue that way, nothing good will happen, unfortunately :(
It feels like a bit of history is missing... If ollama was founded 3 years before llama.cpp was released, what engine did they use then? When did they transition?
I don't think that is the case. Llama.cpp appeared within weeks after meta released llama to select researchers (which then made it out to the public). 3 years before that nobody knew of the name llama. I'm sure that llama.cpp existed first
The name "llama.cpp" doesn't seem very friendly anymore nowadays... Back then, "llama" probably referred to those models from Facebook, and now those Llama series models clearly can't represent the strongest open-source models anymore...
Doesn't the "llama" in "ollama" present exactly the same issue?
Edit: or maybe that was your point. I guess that for historical reasons this is a kind of generic name for local deployments now (see https://www.reddit.com/r/LocalLLaMA) just like people will call anything ChatGPT.
I noticed the performance issues too. I started using Jan recently and tried running the same model via llama.cpp vs local ollama, and the llama.cpp one was noticeably faster.
It's a joke... but also not really? I mean VLC is "just" an interface to play videos. Videos are content files one "interact" with, mostly play/pause and few other functions like seeking. Because there are different video formats VLC relies on codecs to decode the videos, so basically delegating the "hard" part to codecs.
Now... what's the difference here? A model is a codec, the interactions are sending text/image/etc to it, output is text/image/etc out. It's not even radically bigger in size as videos can be huge, like models.
I'm confused as why this isn't a solved problem, especially (and yes I'm being a big sarcastic here, can't help myself) in a time where "AI" supposedly made all smart wise developers who rely on it 10x or even 1000x more productive.
What problem is it that you are confused isn't solved?
I think the codec analogy is neat but isn't the codec here llama.cpp, and the models are content files? Then the equivalent of VLC are things like LMStudio etc. which use llama.cpp to let you run models locally?
I'd guess one reason we haven't solved the "codec" layer is that there doesn't seem to be a standard that open model trainers have converged on yet?
The CLI is great locally, but the architecture fights you in production. Putting a stateful daemon that manages its own blob storage inside a container is a classic anti-pattern. I ended up moving to a proper stateless binary like llama-server for k8s.
I think the biggest advantage for me with ollama is the ability to "hotswap" models with different utility instead of restarting the server with different models combined with the simple "ollama pull model". In other words, it has been quite convenient.
Due to this post I had to search a bit and it seems that llama.cpp recently got router support[1], so I need to have a look at this.
My main use for this is a discord bot where I have different models for different features like replying to messages with images/video or pure text, and non reply generation of sentiment and image descriptions. These all perform best with different models and it has been very convenient for the server to just swap in and out models on request.
I was pretty big on ollama, it seemed like a great default solution. I had alpha that it was a trash organization but I didn't listen because I just liked having a reliable inference backend that didn't require me to install torch. I switched to llama.cpp for everything maybe 6 months ago because of how fucking frustrating every one of my interactions with ollama (the organization) were. I wanna publicly apologize to everyone who's concerns I brushed off. Ollama is a vampire on the culture and their demise cannot come soon enough.
FWIW llama.cpp does almost everything ollama does better than ollama with the exception of model management, but like, be real, you can just ask it to write an API of your preferred shape and qwen will handle it without issue.
> Ollama is a Y Combinator-backed (W21) startup, founded by engineers who previously built a Docker GUI that was acquired by Docker Inc. The playbook is familiar: wrap an existing open-source project in a user-friendly interface, build a user base, raise money, then figure out monetization.
The progression follows the pattern cleanly:
1. Launch on open source, build on llama.cpp, gain community trust
2. Minimize attribution, make the product look self-sufficient to investors
3. Create lock-in, proprietary model registry format, hashed filenames that don’t work with other tools
4. Launch closed-source components, the GUI app
5. Add cloud services, the monetization vector
With Ollama, the initial one-time setup is a little easier, and the CLI is useful, but is it worth dysfunctional templates, worse performance, and the other issues? Not to me.
Jinja templates are very common, and Jinja is not always losslessly convertible to the Go template syntax expected by Ollama. This means that some models simply cannot work correctly with Ollama. Sometimes the effects of this incompatibility are subtle and unpredictable.
> This creates a recurring pattern on r/LocalLLaMA: new model launches, people try it through Ollama, it’s broken or slow or has botched chat templates, and the model gets blamed instead of the runtime.
Seems like maybe, at least some of the time, you’re being underwhelmed my ollama not the model.
The better performance point alone seems worth switching away
I follow the llama.cpp runtime improvements and it’s also true for this project. They may rush a bit less but you also have to wait for a few days after a model release to get a working runtime with most features.
Does it have a model registry with an API and hot swapping or you still have to use sometime like llama swap as suggested in the article ? Or is it CLI?
vLLM isn't suitable for people running LLMs side-by-side with regular applications on their PC. It is very good at hosting LLMs for production on dedicated servers. For the prod usecase ollama/llamacpp are practically useless (but that's ok - it's not the projects goal to be).
I find the style of writing incredibly annoying (it doesn't make the point, full of hyperbole) and the website has the standard slopsite black background and glowing CSS.
That's because it was fully written by an LLM, as usual lately with all the articles on the front page of HN.
No wonder I get downvoted to hell every time I mention this... People here can't even tell anymore. They just find this horrible slop completely normal. HN is just another dead website filled with slop articles, time to move on to some smaller reddit communities...
Another scummy YCombinator project, one of many lately. Looks like no-one is left at the wheel, at least as long as the valuations (and hence money) keep coming in.
One command, and you are running the models even with the rocm drivers without knowing.
If llama provides such UX, they failed terrible at communicating that. Starting with the name. Llama.cpp: that's a cpp library! Ollama is the wrapper. That's the mental model. I don't want to build my own program! I just want to have fun :-P
brew install llama.cpp
llama-server -hf ggml-org/gemma-4-E4B-it-GGUF --port 8000
Go to localhost:8000 for the Web UI. On Linux it accelerates correctly on my AMD GPU, which Ollama failed to do, though of course everyone's mileage seems to vary on this.
llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma4' llama_model_load_from_file_impl: failed to load model
In fact the first line of the wikipedia article is:
> llama.cpp is an open source software library
It makes a bunch of decisions for you so you don't have to think much to get a model up and running.
>One command
Notwithstanding the fact that there's about zero difference between `ollama run model-name` and `llama-cpp -hf model-name`, and that running things in the terminal is already a gigantic UX blocker (Ollama's popularity comes from the fact that it has a GUI), why are you putting the blame back on an open source project that owes you approximately zero communication ?
It's not the GUI, it's the curated model hosting platform. Way easier to use than HF for casual users.
https://www.youtube.com/watch?v=HaF-nRS_CWM
There are packages for Vulkan, ROCm and CUDA. They all work.
I started with Ollama, and it was great. But I moved to llama.cpp to have more up-to-date fixes. I still use Ollama to pull and list my models because it's so easy. I then built my own set of scripts to populate a separate cache directory of hardlinks so llama-swap can load the gguf's into llama.cpp.
I’m open to suggestions, but the alternatives outlined in the blog post ain’t it.
> LM Studio gives you a GUI if that’s what you want. It uses llama.cpp under the hood, exposes all the knobs, and supports any GGUF model without lock-in.
> Jan(https://www.jan.ai/) is another open-source desktop app with a clean chat interface and local-first design.
> Msty(https://msty.ai/) offers a polished GUI with multi-model support and built-in RAG. koboldcpp is another option with a web UI and extensive configuration options.
API wise: LM Studio has REST, OpenAI and more API Compatibilities.
llama-server -hf ggml-org/gemma-4-E4B-it-GGUF --port 8000 (with MCP support and web chat interface)
and you have OpenAI API on the same 8000 port. (https://github.com/ggml-org/llama.cpp/tree/master/tools/serv... lists the endpoints)
Just in case you haven't seen it yet, llama.cpp now has a router mode that lets you hot-swap models. I've switched over from llama-swap and have been happy with it.
Easier than what?
I came across LM Studio (mentioned in the post) about 3 years ago before I even knew what Ollama as. It was far better even then.
It’s truly open source, backed by Mozilla, openly uses llama.cpp and was created by wizard Justine Tunney of CosmopolitanC fame.
--
-- --At the time I dropped it for LMStudio, which to be fair was not fully open source either, but at least exposed the model folder and integrated with HF rather than a proprietary model garden for no good reason.
Actually they do. It's environment variable OLLAMA_MODELS in the server configuration file.
Had to go down the same rabbit hole of finding where things are, how they're sorted/separated/etc. It was unnecessarily painful
Clients get disappointed, alternatives have better services, and more are popping out monthly. If they continue that way, nothing good will happen, unfortunately :(
Ollama v0.0.1 "Fast inference server written in Go, powered by llama.cpp" https://github.com/ollama/ollama/tree/v0.0.1
Edit: or maybe that was your point. I guess that for historical reasons this is a kind of generic name for local deployments now (see https://www.reddit.com/r/LocalLLaMA) just like people will call anything ChatGPT.
It's a joke... but also not really? I mean VLC is "just" an interface to play videos. Videos are content files one "interact" with, mostly play/pause and few other functions like seeking. Because there are different video formats VLC relies on codecs to decode the videos, so basically delegating the "hard" part to codecs.
Now... what's the difference here? A model is a codec, the interactions are sending text/image/etc to it, output is text/image/etc out. It's not even radically bigger in size as videos can be huge, like models.
I'm confused as why this isn't a solved problem, especially (and yes I'm being a big sarcastic here, can't help myself) in a time where "AI" supposedly made all smart wise developers who rely on it 10x or even 1000x more productive.
Weird.
I think the codec analogy is neat but isn't the codec here llama.cpp, and the models are content files? Then the equivalent of VLC are things like LMStudio etc. which use llama.cpp to let you run models locally?
I'd guess one reason we haven't solved the "codec" layer is that there doesn't seem to be a standard that open model trainers have converged on yet?
Due to this post I had to search a bit and it seems that llama.cpp recently got router support[1], so I need to have a look at this.
My main use for this is a discord bot where I have different models for different features like replying to messages with images/video or pure text, and non reply generation of sentiment and image descriptions. These all perform best with different models and it has been very convenient for the server to just swap in and out models on request.
[1] https://huggingface.co/blog/ggml-org/model-management-in-lla...
The article mentions llama-swap does this
FWIW llama.cpp does almost everything ollama does better than ollama with the exception of model management, but like, be real, you can just ask it to write an API of your preferred shape and qwen will handle it without issue.
I will switch once we have good user experience on simple features.
A new model is released on HF or the Ollama registry? One `ollama pull` and it's available. It's underwhelming? `ollama rm`.
`rm [FILE_NAME]`
With Ollama, the initial one-time setup is a little easier, and the CLI is useful, but is it worth dysfunctional templates, worse performance, and the other issues? Not to me.
Jinja templates are very common, and Jinja is not always losslessly convertible to the Go template syntax expected by Ollama. This means that some models simply cannot work correctly with Ollama. Sometimes the effects of this incompatibility are subtle and unpredictable.
Seems like maybe, at least some of the time, you’re being underwhelmed my ollama not the model.
The better performance point alone seems worth switching away
https://github.com/ggml-org/llama.cpp/blob/master/tools/serv...
No wonder I get downvoted to hell every time I mention this... People here can't even tell anymore. They just find this horrible slop completely normal. HN is just another dead website filled with slop articles, time to move on to some smaller reddit communities...
also you might be the only person in the wild I've seen admit to this