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.
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
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.
> 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?
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.
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.
All of those options were either too slow, or didnt work for me (Mac with Intel). I could have spent hours googling, but I downloaded Ollama and it just worked.
LMStudio is listed as an alternative. It offers a chat UI, a model server supporting OpenAI, Anthropic and LMStudio API interfaces. It supports loading the models on demand or picking what models you want loaded. And you can tweak every parameter.
And it uses llama.cpp which is the whole point of the blog post.
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.
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.
>No mention of the fact that Ollama is about 1000x easier to use
I remember changing the context size from the default unusable 2k to something bigger the model actually supports required creating a new model file in Ollama if you wanted the change to persist (another alternative: set an env var before running ollama; although, if you go that low-level route, why not just launch llama.cpp). How was that easier? Did they change this?
I remember people complaining model X is "dumb" simply because Ollama capped the context size to a ridiculously small number by default.
IMHO trying to model Ollama after Docker actually makes it harder for casual users. And power users will have it easier with llama.cpp directly
Not like it mattered much to me but llama-cpp is way lighter and 10x smaller in size.
Resumable downloads seem to work better in llama-cpp.
I love the inbuilt GUI.
I used ollama first and honestly, llama-cpp has been a much better experience.
Maybe given enough time, I would have seen the benefit of ollama but the inability to turn off updates even after users requested it extensively made me uninstall it. Postman PTSD is real.
Koboldcpp is a single executable with a GUI launcher and a built in webui. It also supports tts, stt, image gen, embeddings, music creation, and a bunch of other stuff out of the box, and can download and browse HF models from within the GUI. That's pretty easy to use.
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
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?
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
LlamaBarn is the MacOS app, not the HTTP API server, which is "llama-server".
On non-Apple PCs, "llama-server" is what you use, and you can connect to it either with a browser or with an application compatible with the OpenAI API.
Perhaps using "llama-server" as the name of the project would have been less confusing for newbies than "llama.cpp".
I confess that when I first heard about "llama.cpp" I also thought that it is just a library and that I have to write my own program in order to implement a complete LLM inference backend.
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.
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
Edit: @below, I used `nix-shell -p llama-cpp` so not brew related. Could indeed be an older version indeed! I'll check.
I just hit that error a few minutes ago. I build my llama.cpp from source because I use CUDA on Linux. So I made the mistake of trying to run Gemma4 on an older version I had and I got the same error. It’s possible brew installs an older version which doens’t support Gemma4 yet.
And that's exactly why llama.cpp is not usable by casual users. They follow the "move fast and break things" model. With ollama, you just have to make sure you're getting/building the latest version.
Its not possible to run the latest model architectures without 'moving fast'. The only thing broken here is that they are trying to use an old version with a new model.
As it has been discussed in a few recent threads on HN, whenever a new model is released, running it successfully may need changes in the inference backends, such as llama.cpp.
There are 2 main reasons. One is the tokenizer, where new tokenizer definitions may be mishandled by the older tokenizer parsers.
The second reason is that each model may implement differently the tool invocations, e.g. by using different delimiter tokens and different text layouts for describing the parameters of a tool invocation.
Therefore running the Gemma-4 models encountered various problems during the first days after their release, especially for the dense 31B model.
Solving these problems required both a new version of llama.cpp (also for other inference backends) and updates in the model chat template and tokenizer configuration files.
So anyone who wants to use Gemma-4 should update to the latest version of llama.cpp and to the latest models from Huggingface, because the latest updates have been a couple of days ago.
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 ?
> Notwithstanding the fact that there's about zero difference between `ollama run model-name` and `llama-cpp -hf model-name`
There is a TON of difference. Ollama downloads the model from its own model library server, sticks it somewhere in your home folder with a hashed name and a proprietary configuration that doesn't use the in built metadata specified by the model creator. So you can't share it with any other tool, you can't change parameters like temp on the fly, and you are stuck with whatever quants they offer.
agree. We can easily compare it with docker. Of course people can use runc directly, but most people select not to and use `docker run` instead.
And you can blame docker in a similar manner. LXC existed for at least 5 years before docker. But docker was just much more convenient to use for an average user.
UX is a huge factor for adoption of technology. If a project fails at creating the right interface, there is nothing wrong with creating a wrapper.
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...
> 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
Have you ever tried going to the model registry and seeing that the model was recently updated? What updated? What changed? Should I re-download this 20GB file?
I guess if you're not frustrated with things like this then sure, no need to stop using it.
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.
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).
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.
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.
llama.cpp was already public by March 10, 2023. Ollama-the-company may have existed earlier through YC Winter 2021, but that is not the same thing as having a public local-LLM runtime before llama.cpp. In fact, Ollama’s own v0.0.1 repo says: “Run large language models with llama.cpp” and describes itself as a “Fast inference server written in Go, powered by llama.cpp.” Ollama’s own public blog timeline then starts on August 1, 2023 with “Run Llama 2 uncensored locally,” followed by August 24, 2023 with “Run Code Llama locally.” So the public record does not really support any “they were doing local inference before llama.cpp” narrative.
And that is why the attribution issue matters. If your public product is, from day one, a packaging / UX / distribution layer on top of upstream work, then conspicuous credit is not optional. It is part of the bargain. “We made this easier for normal users” is a perfectly legitimate contribution. But presenting that contribution in a way that minimizes the upstream engine is exactly what annoys people.
The founders’ pre-LLM background also points in the same direction. Before Ollama, Jeffrey Morgan and Michael Chiang were known for Kitematic, a Docker usability tool acquired by Docker on March 13, 2015. So the pattern that fits the evidence is not “they pioneered local inference before everyone else.” It is “they had prior experience productizing infrastructure, then applied that playbook to the local-LLM wave once llama.cpp already existed.”
So my issue is not that Ollama is a wrapper. Wrappers can be useful. My issue is that they seem to have taken the social upside of open-source dependence without showing the level of visible credit, humility, and ecosystem citizenship that should come with it. The product may have solved a real UX problem, but the timeline makes it hard to treat them as if they were the originators of the underlying runtime story.
They seem very good at packaging other people’s work, and not quite good enough at sounding appropriately grateful for that fact.
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.
> 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
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 :(
Yeah my thoughts exactly. Definitely slop. I have no objection to using AI to help writing. I just don't want to read the same sloppy cliches again and again and again. The short sentences. The Bigger Picture. Here's the rub. It's not just A, it's B.
It's like those cliche titles - for fun and profit, the unreasonable effectiveness of, all you need is, etc. etc. but throughout the prose. Stop it guys!
Sure. Short sentences like "It shouldn’t be.", "I’ve moved on.", "Ollama didn’t.", etc.
Not-this-but-that like "The local LLM ecosystem doesn’t need Ollama. It needs llama.cpp."
Weird signposting: "Benchmarks tell the story."
Heres-the-rub conclusion: "The Bigger Picture"
Starting every title with "The ...".
It's definitely largely human-written, but there are enough slop-isms to make it annoying to read. And of course it's totally possible for a human to write an an AI style, but that doesn't make it any less annoying.
Has anybody figured some of the best flags to compile llama.cpp for rocm? I'm using the framework desktop and the Vulkan backend, because it was easier to compile out of the box, but I feel there's large peformance gains on the table by swtiching to rocm. Not sure if installing with brew on ubuntu would be easier.
This is a bit like saying stop using Ubuntu, use Debian instead.
Both llama.cpp and ollama are great and focused on different things and yet complement each other (both can be true at the same time!)
Ollama has great ux and also supports inference via mlx, which has better performance on apple silicon than llama.cpp
I'm using llama.cpp, ollama, lm studio, mlx etc etc depending on what is most convenient for me at the time to get done what I want to get done (e.g. a specific model config to run, mcp, just try a prompt quickly, …)
They might not use the word, but the behavior they describe is evil:
"
This isn’t a matter of open-source etiquette, the MIT license has exactly one major requirement: include the copyright notice. Ollama didn’t.
The community noticed. GitHub issue #3185 was opened in early 2024 requesting license compliance. It went over 400 days without a response from maintainers. When issue #3697 was opened in April 2024 specifically requesting llama.cpp acknowledgment, community PR #3700 followed within hours. Ollama’s co-founder Michael Chiang eventually added a single line to the bottom of the README: “llama.cpp project founded by Georgi Gerganov.”
"
> Both llama.cpp and ollama are great and focused on different things and yet complement each other
According to the article, ollama is not great (that’s an understatement), focused on making money for the company, stealing clout and nothing else, and hardly complements llama.cpp at all since not long after the initial launch. All of these are backed by evidence.
You may disagree, but then you need to refute OP’s points, not try to handwave them away with a BS analogy that’s nothing like the original.
> This is a bit like saying stop using Ubuntu, use Debian instead.
Not really, because Ubuntu has always acknowledged Debian and explicitly documented the dependency:
> Debian is the rock on which Ubuntu is built.
> Ubuntu builds on the Debian architecture and infrastructure and collaborates widely with Debian developers, but there are important differences. Ubuntu has a distinctive user interface, a separate developer community (though many developers participate in both projects) and a different release process.
Ah man the VC death trap. It's ok. I don't mean it like that but this is classic. It's unavoidable. They gotta make money. They took money, they gotta make money. It's not easy. Everyone has principles, developers more than anyone. They are developers, they are people like you and me. They didn't even start as ollama. They started as a kubernetes infra project in YC and pivoted. Listen don't be hard on these guys. It's hard enough. Trust me I did it. And not as well them.
This is the game. We shouldn't delude ourselves into thinking there are alternative ways to become profitable around open source, there aren't. You effectively end up in this trap and there's no escape and then you have to compromise on everything to build the company, return the money, make a profit. You took people's money, now you have to make good, there's no choice. And anyone who thinks differently is deluded. Open source only goes one way. To the enterprise. Everything else is burning money and wasting time. Look at Docker. Textbook example of the enormous struggle to capture the value of a project that had so much potential, defined an industry and ultimately failed. Even the reboot failed. Sorry. It did.
This stuff is messy. Give them some credit. They give you an epic open source project. Be grateful for that. And now if you want to move on, move on. They don't need a hard time. They're already having a hard time. These guys are probably sweating bullets trying to make it work while their investors breathe down their necks waiting for the payoff. Let them breathe.
> This stuff is messy. Give them some credit. They give you an epic open source project.
It seems to me the epic open source project was given to us by Georgi Gerganov. These people just tried to milk it for some money, and made everything a little worse in the process.
So, on a mac, what good alternative to ollama supports mlx for acceleration? My main use case is that I have an old m1 max macbook pro with 64 gb ram that I use as a model server.
I'm sorry, on a mac, Ollama just works. It lets me use a model and test it quickly. This is like saying stop using google drive, upload everything to s3 instead!
When i'm using Ollama - I honeslty don't care about performance, I'm looking to try out a model and then if it seems good, place it onto a most dedicated stack specifically for it.
Ollama is a bit easier to use, you’re right. But the point of the article is the way they just disregarded the license of llama.cpp, moved away from open source while still claiming to be open source and pivoted to cloud offerings when the whole point was to run local models all while without contributing anything back to the big open source projects it owns its existence to. Maybe you don’t care about performance (weird given performance is the main blocker for local LLMs) but you should care about the ethics of companies making the product you use?
And anyway this thread has lots of alternatives that are even easier to use and don’t shit on the open source community making things happen.
I'm making more of a pragmatic point. While ethics of companies are important, i'm still using OpenAI, Anthropic, Microsoft, Apple etc, so I definitely accept a trade-off between morality and ease-of-use.
Currently i've found Ollama to have the best intuitive experience for trying new models. Once i've tried those models and decide on something to use for a project, I can deploy them, and not need to use a UI again.
I'll be trying out the other options in this thread, but my point is that ease of use is going to triumph over the other points the original post made, and some of the alternatives mentioned in the original post miss why Ollama is so popular.
Keep in mind that as the post says, the model you’re trying via ollama may not be the model you asked for! And the performance may be subpar and not reflect the model true performance. Otherwise, I agree they offer an easy and polished product and that explains why they are so popular, besides their personal connections having resulted in their OpenAI partnership.
1. MIT-style licenses are "do what you want" as long as you provide a single line of attribution. Including building big closed source business around it.
2. MIT-style licenses are "do what you want" under the law, but they carry moral, GPL-like obligations to think about the "community."
To my knowledge Georgi Gerganov, the creator of llama.cpp, has only complained about attribution when it was missing. As an open-source developer, he selected a permissive license and has not complained about other issues, only the lack of credit. It seems he treats the MIT license as the first kind.
The article has other good points not related to licensing that are good to know. Like performance issues and simplicity that makes me consider llama.cpp.
The second interpretation is nonsense of course. If you want GPL-like obligations, use the GPL.
A license is what it says in the license, nothing extra. It's a legal document not a moral guideline.
I do think it's a very good idea to always use the GPL (even though commercially minded types always get their panties in a bunch about the GPL) for any user-facing software, to force everybody to 'play fair and share'. The only reason to use MIT imho is for a library implementing some sort of standard where you want that standard used by as many people as possible.
I don't understand people who use MIT for their project and then complain some commercial firm takes their contributions and runs with it. If that's not what you want don't use that license.
Apart from license terms and moral obligations being a bad mix, companies don't have morals. Don't get me wrong, I think they should have! But they don't.
People have morals. Groups of people (a company, a country , a mob) not so much. Sadly.
MIT license lets you do what you want with the code. That's the deal.
The blob storage thing is the real problem though. Nobody talks about it until they try to move their models somewhere else.
Well, yeah, which is why it's silly when people use MIT licenses and then complain that those, for example, with the motto "Build > ask. Disrupt or die.", only take and don't contribute anything back, instead of using a license that demands it.
Georgi could have switched to GPL whenever he wanted. He didn't. That's the answer.
The loudest voices here aren't the ones writing the code. Meanwhile both projects kept shipping and users got more options. Hard to see the harm.
I always avoided Ollama because it smelled like a project that was trying so desperately to own the entire workflow. I guess I dodged a bigger bullet than I knew.
I stopped using Ollama a couple of months ago. Not out of frustration, but because llama.cpp has improved a lot recently with router mode, hot-swapping, a modern and simple web UI, MCP support and lots of other improvements.
I'm a llama.cpp user, but apart from the MIT licensing issue, I personally don't see what's the problem here is? Sure Ollama could have advertised better that llama.cpp was it's original backend, but were they obligated to? It's no different to Docker or VMWare that hitch a ride on kernel primitives etc.
the article buries what's actaully the most practical gotcha: ollama's hashed blob storage means if you've been pulling models for months, switching tools requires re-downloading everything because you can't just point another runtime at those files, and most users won't discover this until they're already invested enough that it genuinely hurts to leave.
I like Ollama Cloud service (I'm paid pro user), because it let me test several open source LLMs very fast - I dont need to download anything locally, just change the model name in the API. If I like the model then I can download it and run locally with sensitive data. I also like their CLI, because it is simple to use.
The fact that they are trying to make money is normal - they are a company. They need to pay the bills.
I agree that they should improve communication, but I assume it is still small company with a lot of different requests, and some things might be overlooked.
Overall I like the software and services they provide.
> the file gets copied into Ollama’s hashed blob storage, you still can’t share the GGUF with other tool
This is the reason I had stopped using it. I think they might be doing it for deduplication however it makes it impossible to use the same model with other tools. Every other tool can just point to the same existing gguf and can go. Whether its their intention or not, it's making it difficult to try out other tools. Model files are quite large as you know and storage and download can become issues. (They are for me)
It is a bit off-topic, but would it possible to provide a light mode for this blog? I used to work during the day time, and my pupils had to contract to read, making it a very poor reading experience.
[OP] Zetaphor | 8 hours ago
robot-wrangler | 5 hours ago
kelsolaar | 5 hours ago
brabel | 4 hours ago
It’s truly open source, backed by Mozilla, openly uses llama.cpp and was created by wizard Justine Tunney of CosmopolitanC fame.
cachius | 3 hours ago
Mario9382 | 4 hours ago
julien_c | 54 minutes ago
uh actually, _we_ did (generates a Docker-style manifest on the fly)
usernomdeguerre | 6 hours ago
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.
andreidbr | 5 hours ago
Had to go down the same rabbit hole of finding where things are, how they're sorted/separated/etc. It was unnecessarily painful
zozbot234 | 3 hours ago
Actually they do. It's environment variable OLLAMA_MODELS in the server configuration file.
ekianjo | 2 hours ago
zozbot234 | 2 hours ago
dnnddidiej | 5 hours ago
tyfon | 5 hours ago
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...
segmondy | 5 hours ago
majorchord | 5 hours ago
The article mentions llama-swap does this
hacker_homie | 4 hours ago
ekianjo | an hour ago
dackdel | 5 hours ago
sudb | 4 hours ago
also you might be the only person in the wild I've seen admit to this
yokoprime | 5 hours ago
speedgoose | 5 hours ago
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`.
kennywinker | 4 hours ago
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
speedgoose | 4 hours ago
Maxious | 4 hours ago
pheggs | 4 hours ago
speedgoose | 4 hours ago
dminik | 4 hours ago
https://github.com/ggml-org/llama.cpp/blob/master/tools/serv...
speedgoose | 3 hours ago
derrikcurran | 3 hours ago
`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.
ekianjo | 2 hours ago
speedgoose | an hour ago
I see quite a few versions, and I can also use hugging face models.
0xbadcafebee | 5 hours ago
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.
AndroTux | 4 hours ago
I’m open to suggestions, but the alternatives outlined in the blog post ain’t it.
mentalgear | 4 hours ago
> 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.
shantnutiwari | an hour ago
So no, they are not alternatives to ollama
homarp | 3 hours ago
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)
Philip-J-Fry | 2 hours ago
LMStudio is listed as an alternative. It offers a chat UI, a model server supporting OpenAI, Anthropic and LMStudio API interfaces. It supports loading the models on demand or picking what models you want loaded. And you can tweak every parameter.
And it uses llama.cpp which is the whole point of the blog post.
BrissyCoder | 4 hours ago
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.
throw9393rj | 4 hours ago
flux3125 | 3 hours ago
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.
kgeist | 2 hours ago
I remember changing the context size from the default unusable 2k to something bigger the model actually supports required creating a new model file in Ollama if you wanted the change to persist (another alternative: set an env var before running ollama; although, if you go that low-level route, why not just launch llama.cpp). How was that easier? Did they change this?
I remember people complaining model X is "dumb" simply because Ollama capped the context size to a ridiculously small number by default.
IMHO trying to model Ollama after Docker actually makes it harder for casual users. And power users will have it easier with llama.cpp directly
rowendduke | 2 hours ago
Resumable downloads seem to work better in llama-cpp.
I love the inbuilt GUI.
I used ollama first and honestly, llama-cpp has been a much better experience.
Maybe given enough time, I would have seen the benefit of ollama but the inability to turn off updates even after users requested it extensively made me uninstall it. Postman PTSD is real.
Eisenstein | an hour ago
fy20 | 4 hours ago
Maxious | 4 hours ago
Ollama v0.0.1 "Fast inference server written in Go, powered by llama.cpp" https://github.com/ollama/ollama/tree/v0.0.1
em-bee | 3 hours ago
doing what?
trying to build themselves what llama.cpp ended up doing for them?
saghul | 2 hours ago
wolvoleo | 4 hours ago
kgwgk | 2 hours ago
One week, really, if we consider the "public" availability.
Llama announced: February 24, 2023
Weights leaked: March 3, 2023
Llama.cpp: March 10, 2023
(Ollama 0.0.1: Jul 8, 2023)
TomGarden | 4 hours ago
osmsucks | 4 hours ago
utopiah | 4 hours ago
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.
sudb | 4 hours ago
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?
imtringued | 3 hours ago
cientifico | 4 hours ago
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
anakaine | 4 hours ago
OtherShrezzing | 4 hours ago
RobotToaster | 4 hours ago
homarp | 2 hours ago
adrian_b | 16 minutes ago
On non-Apple PCs, "llama-server" is what you use, and you can connect to it either with a browser or with an application compatible with the OpenAI API.
Perhaps using "llama-server" as the name of the project would have been less confusing for newbies than "llama.cpp".
I confess that when I first heard about "llama.cpp" I also thought that it is just a library and that I have to write my own program in order to implement a complete LLM inference backend.
eterm | 3 hours ago
In fact the first line of the wikipedia article is:
> llama.cpp is an open source software library
figassis | 3 hours ago
nikodunk | 4 hours ago
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.
teekert | 3 hours ago
llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma4' llama_model_load_from_file_impl: failed to load model
Edit: @below, I used `nix-shell -p llama-cpp` so not brew related. Could indeed be an older version indeed! I'll check.
roosgit | 2 hours ago
zozbot234 | 2 hours ago
Eisenstein | an hour ago
cyanydeez | 57 minutes ago
teekert | 2 hours ago
I'm now on:
$ llama --version version: 8770 (82764d8) built with GNU 15.2.0 for Linux x86_64
(From Nix unstable)
And this works as advertised, nice chat interface, but no openai API I guess, so no opencode...
homarp | 2 hours ago
teekert | 2 hours ago
adrian_b | 21 minutes ago
There are 2 main reasons. One is the tokenizer, where new tokenizer definitions may be mishandled by the older tokenizer parsers.
The second reason is that each model may implement differently the tool invocations, e.g. by using different delimiter tokens and different text layouts for describing the parameters of a tool invocation.
Therefore running the Gemma-4 models encountered various problems during the first days after their release, especially for the dense 31B model.
Solving these problems required both a new version of llama.cpp (also for other inference backends) and updates in the model chat template and tokenizer configuration files.
So anyone who wants to use Gemma-4 should update to the latest version of llama.cpp and to the latest models from Huggingface, because the latest updates have been a couple of days ago.
mijoharas | 4 hours ago
It makes a bunch of decisions for you so you don't have to think much to get a model up and running.
FrozenSynapse | 4 hours ago
UqWBcuFx6NV4r | 2 hours ago
amelius | 3 hours ago
mech422 | 3 hours ago
https://www.youtube.com/watch?v=HaF-nRS_CWM
well_ackshually | 3 hours ago
>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 ?
zozbot234 | 3 hours ago
It's not the GUI, it's the curated model hosting platform. Way easier to use than HF for casual users.
kgwgk | 2 hours ago
Eisenstein | an hour ago
There is a TON of difference. Ollama downloads the model from its own model library server, sticks it somewhere in your home folder with a hashed name and a proprietary configuration that doesn't use the in built metadata specified by the model creator. So you can't share it with any other tool, you can't change parameters like temp on the fly, and you are stuck with whatever quants they offer.
samus | 2 hours ago
Re curation: they should strive to not integrate broken support for models and avoid uploading broken GGUFs.
ekianjo | 2 hours ago
This does not absolve them from the license violation
croes | 2 hours ago
omgitspavel | 2 hours ago
And you can blame docker in a similar manner. LXC existed for at least 5 years before docker. But docker was just much more convenient to use for an average user.
UX is a huge factor for adoption of technology. If a project fails at creating the right interface, there is nothing wrong with creating a wrapper.
arcza | 4 hours ago
Karuma | 3 hours ago
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...
arcza | 2 hours ago
mentalgear | 4 hours ago
goodpoint | 4 hours ago
dhruv3006 | 4 hours ago
abhikul0 | 57 minutes ago
I guess if you're not frustrated with things like this then sure, no need to stop using it.
san_tekart | 4 hours ago
DeathArrow | 4 hours ago
StrauXX | 3 hours ago
paganel | 4 hours ago
Havoc | 3 hours ago
NamlchakKhandro | 3 hours ago
NamlchakKhandro | 3 hours ago
denismi | 3 hours ago
mongrelion | 3 hours ago
There are packages for Vulkan, ROCm and CUDA. They all work.
FlyingSnake | 2 hours ago
I just installed llama.cpp on CachyOS after reading this article. It’s much faster and better than Ollama.
thot_experiment | 3 hours ago
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.
ipeev | 3 hours ago
llama.cpp was already public by March 10, 2023. Ollama-the-company may have existed earlier through YC Winter 2021, but that is not the same thing as having a public local-LLM runtime before llama.cpp. In fact, Ollama’s own v0.0.1 repo says: “Run large language models with llama.cpp” and describes itself as a “Fast inference server written in Go, powered by llama.cpp.” Ollama’s own public blog timeline then starts on August 1, 2023 with “Run Llama 2 uncensored locally,” followed by August 24, 2023 with “Run Code Llama locally.” So the public record does not really support any “they were doing local inference before llama.cpp” narrative.
And that is why the attribution issue matters. If your public product is, from day one, a packaging / UX / distribution layer on top of upstream work, then conspicuous credit is not optional. It is part of the bargain. “We made this easier for normal users” is a perfectly legitimate contribution. But presenting that contribution in a way that minimizes the upstream engine is exactly what annoys people.
The founders’ pre-LLM background also points in the same direction. Before Ollama, Jeffrey Morgan and Michael Chiang were known for Kitematic, a Docker usability tool acquired by Docker on March 13, 2015. So the pattern that fits the evidence is not “they pioneered local inference before everyone else.” It is “they had prior experience productizing infrastructure, then applied that playbook to the local-LLM wave once llama.cpp already existed.”
So my issue is not that Ollama is a wrapper. Wrappers can be useful. My issue is that they seem to have taken the social upside of open-source dependence without showing the level of visible credit, humility, and ecosystem citizenship that should come with it. The product may have solved a real UX problem, but the timeline makes it hard to treat them as if they were the originators of the underlying runtime story.
They seem very good at packaging other people’s work, and not quite good enough at sounding appropriately grateful for that fact.
zxcholmes | 3 hours ago
kgwgk | 3 hours ago
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.
abhikul0 | an hour ago
eternaut | 3 hours ago
_bobm | 3 hours ago
mrkeen | 3 hours ago
mrkeen | 3 hours ago
--
-- --sminchev | 3 hours ago
Clients get disappointed, alternatives have better services, and more are popping out monthly. If they continue that way, nothing good will happen, unfortunately :(
dragochat | 3 hours ago
- vLLM https://vllm.ai/ ?
- oMLX https://github.com/jundot/omlx ?
stuaxo | 3 hours ago
At the top could have been a link to equivalent llamacpp workflows to ollamas.
I wish the op had gone back and written this as a human, I agree with not using Ollama but don't like reading slop.
IshKebab | 2 hours ago
It's like those cliche titles - for fun and profit, the unreasonable effectiveness of, all you need is, etc. etc. but throughout the prose. Stop it guys!
audience_mem | an hour ago
IshKebab | 59 minutes ago
Not-this-but-that like "The local LLM ecosystem doesn’t need Ollama. It needs llama.cpp."
Weird signposting: "Benchmarks tell the story."
Heres-the-rub conclusion: "The Bigger Picture"
Starting every title with "The ...".
It's definitely largely human-written, but there are enough slop-isms to make it annoying to read. And of course it's totally possible for a human to write an an AI style, but that doesn't make it any less annoying.
audience_mem | an hour ago
iib | 3 hours ago
ekianjo | an hour ago
tosh | 3 hours ago
Both llama.cpp and ollama are great and focused on different things and yet complement each other (both can be true at the same time!)
Ollama has great ux and also supports inference via mlx, which has better performance on apple silicon than llama.cpp
I'm using llama.cpp, ollama, lm studio, mlx etc etc depending on what is most convenient for me at the time to get done what I want to get done (e.g. a specific model config to run, mcp, just try a prompt quickly, …)
damnitbuilds | 3 hours ago
So it is more like saying "Stop using SCO Unix, use Linux instead".
yuppiepuppie | 3 hours ago
cadamsdotcom | 2 hours ago
damnitbuilds | 2 hours ago
" This isn’t a matter of open-source etiquette, the MIT license has exactly one major requirement: include the copyright notice. Ollama didn’t.
The community noticed. GitHub issue #3185 was opened in early 2024 requesting license compliance. It went over 400 days without a response from maintainers. When issue #3697 was opened in April 2024 specifically requesting llama.cpp acknowledgment, community PR #3700 followed within hours. Ollama’s co-founder Michael Chiang eventually added a single line to the bottom of the README: “llama.cpp project founded by Georgi Gerganov.” "
oefrha | 3 hours ago
According to the article, ollama is not great (that’s an understatement), focused on making money for the company, stealing clout and nothing else, and hardly complements llama.cpp at all since not long after the initial launch. All of these are backed by evidence.
You may disagree, but then you need to refute OP’s points, not try to handwave them away with a BS analogy that’s nothing like the original.
operatingthetan | 3 hours ago
matja | 2 hours ago
Not really, because Ubuntu has always acknowledged Debian and explicitly documented the dependency:
> Debian is the rock on which Ubuntu is built.
> Ubuntu builds on the Debian architecture and infrastructure and collaborates widely with Debian developers, but there are important differences. Ubuntu has a distinctive user interface, a separate developer community (though many developers participate in both projects) and a different release process.
Source: https://ubuntu.com/community/docs/governance/debian
Ollama never has for llama.cpp. That's all that's being asked for, a credit.
UqWBcuFx6NV4r | 2 hours ago
carlostkd | 2 hours ago
damnitbuilds | 3 hours ago
So given, as the author states, Ollama runs the LLMs inefficiently, what is the tool that runs them most efficiently on limited hardware ?
asim | 3 hours ago
This is the game. We shouldn't delude ourselves into thinking there are alternative ways to become profitable around open source, there aren't. You effectively end up in this trap and there's no escape and then you have to compromise on everything to build the company, return the money, make a profit. You took people's money, now you have to make good, there's no choice. And anyone who thinks differently is deluded. Open source only goes one way. To the enterprise. Everything else is burning money and wasting time. Look at Docker. Textbook example of the enormous struggle to capture the value of a project that had so much potential, defined an industry and ultimately failed. Even the reboot failed. Sorry. It did.
This stuff is messy. Give them some credit. They give you an epic open source project. Be grateful for that. And now if you want to move on, move on. They don't need a hard time. They're already having a hard time. These guys are probably sweating bullets trying to make it work while their investors breathe down their necks waiting for the payoff. Let them breathe.
Good luck to you ollama guys!
tasuki | 2 hours ago
It seems to me the epic open source project was given to us by Georgi Gerganov. These people just tried to milk it for some money, and made everything a little worse in the process.
ontouchstart | 23 minutes ago
UX is where the money is, it is in the wrapper, not the core.
Unfortunately, the core is the most valuable and labor intensive part of it.
With agentic coding, the gap between solid core and shitty wrapper is going to be wider and wider.
holliplex | 3 hours ago
zarzavat | 3 hours ago
aquir | 3 hours ago
I was using LM Studio since I've moved to MacOS so that's fine I guess
song | 2 hours ago
wrxd | 2 hours ago
If someone has opinions please let us know!
endymion-light | 2 hours ago
When i'm using Ollama - I honeslty don't care about performance, I'm looking to try out a model and then if it seems good, place it onto a most dedicated stack specifically for it.
brabel | 2 hours ago
And anyway this thread has lots of alternatives that are even easier to use and don’t shit on the open source community making things happen.
endymion-light | 36 minutes ago
Currently i've found Ollama to have the best intuitive experience for trying new models. Once i've tried those models and decide on something to use for a project, I can deploy them, and not need to use a UI again.
I'll be trying out the other options in this thread, but my point is that ease of use is going to triumph over the other points the original post made, and some of the alternatives mentioned in the original post miss why Ollama is so popular.
brabel | 15 minutes ago
rothific | 2 hours ago
WhereIsTheTruth | 2 hours ago
It is a parasitic stack that redirects investment into service wrappers while leaving core infrastructure underfunded
We have to suffer with limits and quotas as if we are living in the Soviet Union
opem | 2 hours ago
renierbotha | 2 hours ago
u1hcw9nx | 2 hours ago
1. MIT-style licenses are "do what you want" as long as you provide a single line of attribution. Including building big closed source business around it.
2. MIT-style licenses are "do what you want" under the law, but they carry moral, GPL-like obligations to think about the "community."
To my knowledge Georgi Gerganov, the creator of llama.cpp, has only complained about attribution when it was missing. As an open-source developer, he selected a permissive license and has not complained about other issues, only the lack of credit. It seems he treats the MIT license as the first kind.
The article has other good points not related to licensing that are good to know. Like performance issues and simplicity that makes me consider llama.cpp.
maybewhenthesun | 2 hours ago
A license is what it says in the license, nothing extra. It's a legal document not a moral guideline.
I do think it's a very good idea to always use the GPL (even though commercially minded types always get their panties in a bunch about the GPL) for any user-facing software, to force everybody to 'play fair and share'. The only reason to use MIT imho is for a library implementing some sort of standard where you want that standard used by as many people as possible.
I don't understand people who use MIT for their project and then complain some commercial firm takes their contributions and runs with it. If that's not what you want don't use that license.
Apart from license terms and moral obligations being a bad mix, companies don't have morals. Don't get me wrong, I think they should have! But they don't.
People have morals. Groups of people (a company, a country , a mob) not so much. Sadly.
WobblyDev | an hour ago
duskdozer | an hour ago
WobblyDev | an hour ago
FeepingCreature | 2 hours ago
flux3125 | 2 hours ago
alfiedotwtf | 2 hours ago
blueybingo | 2 hours ago
pplonski86 | 2 hours ago
The fact that they are trying to make money is normal - they are a company. They need to pay the bills.
I agree that they should improve communication, but I assume it is still small company with a lot of different requests, and some things might be overlooked.
Overall I like the software and services they provide.
nextlevelwizard | 2 hours ago
What is the llama-cpp alternative?
dizhn | 2 hours ago
This is the reason I had stopped using it. I think they might be doing it for deduplication however it makes it impossible to use the same model with other tools. Every other tool can just point to the same existing gguf and can go. Whether its their intention or not, it's making it difficult to try out other tools. Model files are quite large as you know and storage and download can become issues. (They are for me)
rrhjm53270 | an hour ago