The local LLM ecosystem doesn’t need Ollama

https://sleepingrobots.com/dreams/stop-using-ollama/

by Zetaphor • 3 months ago

648 201 3 months ago

Zetaphor8• 3 months ago

I got tired of repeating the same points and having to dig up sources every time, so here's the timeline (as I know it) in one place with sources.

robot-wrangler• 3 months ago

Thanks, did not know any of this.

kelsolaar• 3 months ago

Great writing, thanks for the summary and timeline.

brabel3• 3 months ago

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.

cachius• 3 months ago

I also thought llamafile deserves a mention. Once you have all model params and tunings done bakes 'em into a single portable binary!

Mario9382• 3 months ago

Really nice. I wasn't aware of any of this.

julien_c• 3 months ago

Ollama eventually added ollama run hf.co/{repo}:{quant} to pull directly from Hugging Face, which partially addresses the availability problem.

uh actually, _we_ did (generates a Docker-style manifest on the fly)

Zetaphor8• 3 months ago

Hah! I did not know that, I'll update the article!

kashyapc2• 3 months ago

Thank you; it's an educating read for me, as someone who doesn't dwell in this space, but cares about FOSS in its true spirit.

usernomdeguerre• 3 months ago

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.

andreidbr• 3 months ago

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

zozbot2344• 3 months ago

Do they still not let you change the default model folder?

Actually they do. It's environment variable OLLAMA_MODELS in the server configuration file.

ekianjo5• 3 months ago

Yet people claim it has great UI... And still you can't define that in their GUI...

dnnddidiej• 3 months ago

On a practical note if fumbles connection handling as to be unusable to download anything.

tyfon• 3 months ago

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.

[1] https://huggingface.co/blog/ggml-org/model-management-in-lla...

segmondy• 3 months ago

You can do that with llama-server

majorchord• 3 months ago

the ability to "hotswap" models with different utility instead of restarting the server

The article mentions llama-swap does this

hacker_homie• 3 months ago

Llama.cpp added the ability load/switch models on demand with the max-models and models preset flags.

ekianjo5• 3 months ago

Llama-server which is part of llamacpp does this for a few months now

dackdel• 3 months ago

i use goose by block

sudb2• 3 months ago

seems pretty unrelated to the post?

also you might be the only person in the wild I've seen admit to this

yokoprime• 3 months ago

i had no idea about all this. especially the performance and bugs. thanks for informing me!

speedgoose5• 3 months ago

I prefer Ollama over the suggested alternatives.

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• 3 months ago

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

speedgoose5• 3 months ago

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.

pheggs• 3 months ago

you can pull directly from huggingface with llama.cpp, and it also has a decent web chat included

speedgoose5• 3 months ago

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?

derrikcurran• 3 months ago

`wget https://huggingface.co/[USER]/[REPO]/resolve/main/[FILE_NAME...`

`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.

ekianjo5• 3 months ago

You have no idea what you are downloading with such a pull. At least LMstudio gives you access to all the different versions of the same model.

speedgoose5• 3 months ago

https://ollama.com/library/gemma4/tags

I see quite a few versions, and I can also use hugging face models.

0xbadcafebee2• 3 months ago

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.

AndroTux4• 3 months ago

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.

mentalgear2• 3 months ago

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.

homarp3• 3 months ago

like someone said above: brew install llama.cpp

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• 3 months ago

What do you mean?

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.

adrian_b3• 3 months ago

What you say was true in the past.

As other posters report, now llama-server implements an OpenAI compatible API and you can also connect to it with any Web browser.

I have not tried yet the OpenAI API, but it should have eliminated the last Ollama advantage.

I do not believe that the Ollama "curated" models are significantly easier to use for a newbie than downloading the models directly from Huggingface.

On Huggingface you have much more details about models, which can allow you to navigate through the jungle of countless model variants, to find what should be more suitable for yourself.

The fact criticized in TFA, that the Ollama "curated" list can be misleading about the characteristics of the models, is a very serious criticism from my point of view, which is enough for me to not use such "curated" models.

I am not aware of any alternative for choosing and downloading the right model for local inference that is superior to using directly the Huggingface site.

I believe that choosing a model is the most intimidating part for a newbie who wants to run inference locally.

If a good choice is made, downloading the model, installing llama.cpp and running llama-server are trivial actions, which require minimal skills.

BrissyCoder• 3 months ago

No mention of the fact that Ollama is about 1000x easier to use.

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• 3 months ago

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.

flux31252• 3 months ago

so llama-swap can load

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.

0xbadcafebee2• 3 months ago

I still use llama-swap as its configuration allows me to tailor llama.cpp settings per-model, set autoload timeouts, web ui log viewing, lots of great metrics, and load/unload model on a click. Llama-swap also technically lets you port-forward to some other app/service, like a remote service.

kgeist• 3 months ago

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

rowendduke• 3 months ago

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.

Eisenstein6• 3 months ago

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.

myfakebadcode• 3 months ago

Least friendly you’ve used makes me think you’ve been spoiled. :)

Agreed ollama is a good intro but once you move beyond it starts to be a pain.

kashyapc2• 3 months ago

No mention of the fact that Ollama is about 1000x easier to use.

The point of the article is not to expound on how user-friendly "Ollama" is. It's about exposing the deception and shameful moral low ground they took.

ottah• 3 months ago

It's a lot better than it used to be and honestly way more powerful. If you haven't looked lately, you should https://github.com/ggml-org/llama.cpp/tree/master/tools/serv...

fy20• 3 months ago

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?

Maxious2• 3 months ago

They spent several years in stealth mode but the initial release was llama.cpp.

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 months ago

They spent several years in stealth mode

doing what?

trying to build themselves what llama.cpp ended up doing for them?

wolvoleo• 3 months ago

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

kgwgk3• 3 months ago

within weeks

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• 3 months ago

The performance issues are crazy. Thanks for sharing this

osmsucks• 3 months ago

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.

utopiah• 3 months ago

Not sure why VLC doesn't do that.

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.

sudb2• 3 months ago

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?

imtringued• 3 months ago

llama.cpp is the ffmpeg/libavcodec equivalent in this story.

cientifico• 3 months ago

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

anakaine2• 3 months ago

Llama.cpp now has a gui installed by default. It previously lacked this. Times have changed.

OtherShrezzing• 3 months ago

While that might be true, for as long as its name is “.cpp”, people are going to think it’s a C++ library and avoid it.

nikodunk• 3 months ago

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:

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.

mijoharas• 3 months ago

Frankly I think the cli UX and documentation is still much better for ollama.

It makes a bunch of decisions for you so you don't have to think much to get a model up and running.

zombot• 3 months ago

I don't care about the GUI so much. Ollama lets me download, adjust and run a whole bunch of models and they are reasonably fast. Last time I compared it with Llama.cpp, finding out how to download and install models was a pain in Llama.cpp and it was also _much_ slower than Ollama.

FrozenSynapse• 3 months ago

but if ollama is much slower, that's cutting on your fun and you'll be having better fun with a faster GUI

UqWBcuFx6NV4r2• 3 months ago

You’ve completely missed the point.

amelius• 3 months ago

Whip that llama! Oh wait, that's a different program.

mech422• 3 months ago

LOL

https://www.youtube.com/watch?v=HaF-nRS_CWM

well_ackshually• 3 months ago

solved the UX problem.
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 ?

zozbot2344• 3 months ago

Ollama's popularity comes from the fact that it has a GUI

It's not the GUI, it's the curated model hosting platform. Way easier to use than HF for casual users.

Eisenstein6• 3 months ago

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.

samus• 3 months ago

How about kobold.cpp then? Or LMStudio (I know it's not open source, but at least they give proper credit to llama.cpp)?

Re curation: they should strive to not integrate broken support for models and avoid uploading broken GGUFs.

ekianjo5• 3 months ago

For most users that wanted to run LLM locally, ollama solved the UX problem

This does not absolve them from the license violation

croes• 3 months ago

But if you’re just a GUI wrapper then at least attribute the library you created the GUI for

omgitspavel• 3 months ago

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.

JKCalhoun• 3 months ago

"LM Studio… Jan… Msty… koboldcpp…"

Plenty of alternatives listed. Can anyone with experience suggest the likely successor to Ollama? I have a Mac Mini but don't mind a C/L tool.

I think, as was pointed out, Ollama won because of how easy it is to set up, pull down new models. I would expect similar for a replacement.

Zetaphor8• 3 months ago

If you don't want to have to think about it, LM Studio is probably the best choice.

mentalgear2• 3 months ago

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

goodpoint• 3 months ago

The missing attribution pattern is nasty.

dhruv3006• 3 months ago

ollama is pretty intuitive to use still - dont see why will stop.

abhikul02• 3 months ago

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.

Zetaphor8• 3 months ago

LM Studio is equally as simple, has all the same features, and none of the performance or lock-in problems of ollama.

If you only needed a single reason, how about kneecapping your performance by choosing ollama?

DeathArrow• 3 months ago

I see no mention of vLLM in the article.

StrauXX• 3 months ago

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).

paganel• 3 months ago

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.

Havoc• 3 months ago

Alas people want convenience and don’t care about this sort of stuff.

NamlchakKhandro2• 3 months ago

[flagged]

NamlchakKhandro2• 3 months ago

LM Studio is 1000x easier to use than ollama btw

denismi• 3 months ago

Hmm..

  pacman -Ss ollama | wc -l                                                                                                              
  16
  pacman -Ss llama.cpp | wc -l
  0
  pacman -Ss lmstudio | wc -l
  0

Maybe some day.

mongrelion• 3 months ago

llama.cpp moves too quickly to be added as a stable package. Instead, you can get it directly from AUR: https://aur.archlinux.org/packages?O=0&K=llama.cpp

There are packages for Vulkan, ROCm and CUDA. They all work.

yjftsjthsd-h2• 3 months ago

That doesn't make sense. Why would llama.cpp need to move any faster than ollama? For that matter, why not have a llama.cpp package and llama.cpp-git in the AUR?

FlyingSnake• 3 months ago

yay -S llama.cpp

I just installed llama.cpp on CachyOS after reading this article. It’s much faster and better than Ollama.

tmtvl• 3 months ago

Then again...

  zypper --no-refresh search llamacpp | tail -n5 | wc -l
  5

Sometimes Arch has the software you want at the version you want, other times it doesn't but other distros do. That's why there's half a billion distros instead of just one.

thot_experiment2• 3 months ago

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.

thot_experiment2• 3 months ago

Oh I was completely wrong about the model management stuff btw, llama-server has fully fledged model management baked in now, you just have to make an *.ini with your model configs (most models can do this themselves, I pointed qwen3.6 at the relevant part of the docs and it wrote me an ini with all of my model configs in about 2 minutes) and you can swap between models via api or a dropdown menu in the UI.

ipeev• 3 months ago

The timeline here is pretty important.

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 months ago

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...

kgwgk3• 3 months ago

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.

abhikul02• 3 months ago

https://en.wikipedia.org/wiki/List_of_generic_and_genericize...

eternaut• 3 months ago

the article nails it!

_bobm• 3 months ago

amen

mrkeen2• 3 months ago

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

mrkeen2• 3 months ago

I've now given ramalama a look:

  % ramalama run qwen3.5   
   > hi

  Server or container exited. Shutting down client.

  % ramalama run gemma4:e2b
   > hello

  Server or container exited. Shutting down client.

sminchev• 3 months ago

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 :(

dragochat• 3 months ago

how about the others:

- vLLM https://vllm.ai/ ?

- oMLX https://github.com/jundot/omlx ?

iib• 3 months ago

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.

ekianjo5• 3 months ago

Not huge gains with rocm but you do get a little faster preprocessing speed

tosh• 3 months ago

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, …)

damnitbuilds3• 3 months ago

The author points out that the Ollama people are evil.

So it is more like saying "Stop using SCO Unix, use Linux instead".

yuppiepuppie• 3 months ago

Where do they use the term "evil"?

oefrha• 3 months ago

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.

operatingthetan• 3 months ago

I guess read the article before commenting?

matja• 3 months ago

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.

Source: https://ubuntu.com/community/docs/governance/debian

Ollama never has for llama.cpp. That's all that's being asked for, a credit.

UqWBcuFx6NV4r2• 3 months ago

OK. That says absolutely nothing about actual UX or anything that matters to most actual users (as opposed to argumentative HN ideologues).

carlostkd• 3 months ago

There isn't much you can do with Ollama models besides saying good morning.

damnitbuilds3• 3 months ago

I am trying to run models that are on the edge of what my hardware can support. I guess many people are.

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 months ago

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.

Good luck to you ollama guys!

tasuki• 3 months ago

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.

ontouchstart• 3 months ago

100%.

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.

zarzavat• 3 months ago

It's as if Ollama is trying to create a walled garden, but the garden is outside of their property, so all it achieves is walling themselves in.

aquir• 3 months ago

I did not know! Shady :(

I was using LM Studio since I've moved to MacOS so that's fine I guess

song• 3 months ago

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.

wrxd• 3 months ago

I read good things about https://omlx.ai but I don't really know enough of the ecosystem to know if there are better options.

If someone has opinions please let us know!

Zetaphor8• 3 months ago

LM Studio is a popular option that bundles the MLX backend

endymion-light3• 3 months ago

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.

brabel3• 3 months ago

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.

endymion-light3• 3 months ago

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.

Zetaphor8• 3 months ago

Give LM Studio a shot! It gives you the same experience without all of the problems of Ollama.

rothific• 3 months ago

I've been experimenting with running Gemma with MLX directly within my own harness: https://github.com/cjroth/mlx-harness

WhereIsTheTruth• 3 months ago

The state of LLM as a service is just depressing

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• 3 months ago

There is also lemonade-server from AMD. Although I am not sure if that is any better.

renierbotha• 3 months ago

Thank you, I needed to read this.

u1hcw9nx• 3 months ago

Two Views of MIT-Style Licenses:

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.

maybewhenthesun2• 3 months ago

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.

WobblyDev2• 3 months ago

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.

WobblyDev2• 3 months ago

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.

FeepingCreature• 3 months ago

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.

flux31252• 3 months ago

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.

alfiedotwtf• 3 months ago

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.

blueybingo• 3 months ago

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.

pplonski86• 3 months ago

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.

nextlevelwizard• 3 months ago

I am running ollama as back end and open webui as front end. It handled downloading and swapping between models.

What is the llama-cpp alternative?

dizhn• 3 months ago

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)

rrhjm53270• 3 months ago

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.

rement• 3 months ago

I switched to using LlamaBarn to manage local models on macOS.

https://github.com/ggml-org/llamabarn

erusev• 3 months ago

This is partly why we're building LlamaBarn. It's a lightweight macOS menu bar app that runs llama-server under the hood, with models stored as standard GGUFs in your Hugging Face cache — the same location llama-server uses by default. No separate model store, no lock-in.

https://github.com/ggml-org/LlamaBarn

shantnutiwari2• 3 months ago

Just tried llama.cpp

NO, it is not simpler or even as simple as Ollama.

There are multiple options-- llama server and cli, its not obivous which model to use.

With ollama, its one file. And you get the models from their site, you can browse an easy list.

I dont have the time to go thru 20billlion hugging face models and decide which is the one for me.

Thanks, but I'm sticking with Ollama

4k93n2• 3 months ago

jan.ai would be the ideal route to take here then. its open source, has a simple chat interface, it uses llama.cpp, it lets you search for models and downloads them, and it supports .gguf so youre not locked in if you want to use the models with another program later on

bashbjorn• 3 months ago

Oh hey I'm also working on a thing to solve the devx of llama.cpp: https://github.com/nobodywho-ooo/nobodywho

In contrast to Ollama, this is a self-contained library, not a server.

I wrote some quick notes on this blogpost, just to jot down how we think about good open-source citizenship: https://www.nobodywho.ai/posts/notes-on-friends-dont-let-fri...

MyUltiDev• 3 months ago

The attribution and lock-in arguments are the loud parts of this story, but the quieter production reason to move is concurrency. llama.cpp's server takes parallel N with cont-batching enabled by default, which interleaves tokens from multiple requests inside a single batch and keeps the GPU busy. Ollama defaults its parallel slots low and the interaction is less transparent, so the first time three people share a single model instance you feel it before any of the ethics become relevant. For a 70B Q4_K_M on a workstation, the real ceiling is KV cache fragmentation, and you have to size the context window around the parallel count rather than around one user. What is the highest parallel value anyone here has kept stable on a 70B Q4_K_M before the cache eviction pattern starts hurting quality?

hojinkoh• 3 months ago

ollama is certainly a "useful" tool. I've lost count how many times my non-technical boss (a professor) said "I can run this model in ollama on my notebook, so obviously it's easy to serve and scale."

g023• 3 months ago

I've started creating https://github.com/g023/localmodelrouter/ which offers Ollama like functionality but as a single .py file with minimal dependencies and more focus on letting llama.cpp handle the dirty work.

mastermage2• 3 months ago

thanks for opening my eyes uninstalled ollama, currently trying out LM Studio will probably try the othe ones aswell. the .cpp always scared me from using llama.cpp directly.

anandkrshnn• 3 months ago

Thanks for laying out the timeline so clearly. I’ve been using Ollama for months because it ‘just worked,’ but I had no idea about the missing attribution or the hashed blob storage lock-in. The fact that I can’t easily point another tool at the same GGUF file is a dealbreaker for me. Going to try llama-server today.

The local LLM ecosystem doesn’t need Ollama

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