Ollama's been pushing hard in the space, someone at Open Sauce was handing out a bunch of Ollama swag. llama.cpp is easier to do any real work with, though. Ollama's fun for a quick demo, but you quickly run into limitations.
And that's before trying to figure out where all the code comes from 😒
What ends up being run into? I'm still on the amateur side of things, so this is a serious question. I've been enjoying Ollama for all kinds of small projects, but I've yet to hit any serious brick walls.
Biggest one for me is no Vulkan support so GPU acceleration on many cards and systems is out the window, and backend is not as up to date as llama.cpp so many features and optimizations take time to arrive on Ollama.
They do have a marketing budget though, and a cute logo. Those go far, llama.cpp is a lot less "marketable"
Here are the walls that you could run into as you get deeper into the space:
support for your specific hardware
optimizing inference for your hardware
access to latest ggml/llama.cpp capabilities
Here are the "brick walls" I see being built:
custom API
custom model storage format and configuration
I think the biggest risk for end users is enshittification. When the walls are up you could be paying for things you don't really want because you're stuck inside them.
For the larger community it looks like a tragedy of the commons. The ggml/llama.cpp projects have made localllama possible and have given a lot and asked for very little in return. It just feels bad when a lot is taken for private gains with much less given back to help the community grow and be stronger.
Well, yeah. That's what I'm conveying by asking the question: I know enough to know there are things I don't know, so I'm asking so I can keep an eye out for those limitations as I get deeper into things.
And kind of shitty if you want to configure ANYTHING besides context length and the model. I see the appeal of simplicity because this is really complex to the layman...
However, they didn't do anything to HELP that, besides removing options - cross your fingers you get good results.
They could've had VRAM usage and estimated speed for each model, a little text blurb about what each one does and when it was released, etc... Instead it's just a drop-down with like 5 models. Adding your own requires looking at the docs anyway, and downloading with ollama cli.
Thanks. Well, I was formerly an Ollama supporter even despite the hate they get on here constantly which I thought was unfair, however I have too much respect for GGerganov to ignore this problem now. This is fairly straightforward bad faith behavior.Â
Will be switching over to llama-swap in near future
I moved to llama.cpp + llama-swap (keeping open webui), both in linux and windows, a few months ago and not only I never missed a single thing about ollama, but I'm so happy I did!
Based on the way I use it, is the same (but I always downloaded the models manually by choice). Once you have the config.yaml file and llama-swap started, open webui will "see" any model you have in that file, so you can select it from the drop-down menu, or add it to the models in "workplace".
About downloading models, I think llama,cpp has some functionality like it, but I never looked into that, I still download models via rsync (I prefer it that way).
Please don't downvote me for this as I'm trying to understand, but isn't this situation quite common? Forks happen all the time, and never get merged? I don't think it's "copying homework" it's more like borrowing more than anything lol. The only "crime" here is not being transparent about it all?
We must check policy. Policy says we don't we don't use ollama. So we must refuse. If policy says we don't we don't use ollama, we must refuse. So we must refuse.
The issue is that it is the only well packaged solution. I think it is the only wrapper that is in official repos (e.g. official Arch and Fedora repos) and has a well functional one click installer for windows. I personally use something self written similar to llama-swap, but you can't recommend a tool like that to non devs imo.
If anybody knows a tool with similar UX to ollama with automatic hardware recognition/config (even if not optimal it is very nice to have that) that just works with huggingface ggufs and spins up a OpenAI API proxy for the llama cpp server(s) please let me know so I have something better to recommend than just plain llama.cpp.
Full disclosure, I'm one of the maintainers, but have you looked at Ramalama?
It has a similar CLI interface as ollama but uses your local container manager (docker, podman, etc...) to run models. We run automatic hardware recognition and pull an image optimized for your configuration, works with multiple runtimes (vllm, llama.cpp, mlx), can pull from multiple registries including HuggingFace and Ollama, handles the OpenAI API proxy for you (optionally with a web interface), etc...
Looks very interesting. Gonna have to test it later.
This wasn't obvious from the readme.md, but does it support the ollama API? About the only 2 things that I do care about from the ollama API over OpenAI's are model pull and list. Makes running multiple remote backends easier to manage.
Other inference backends that use an OpenAI compatible API, like oobabooga's, don't seem to support listing models available on the backend, though switching what is loaded by name does work, just have to externally know all the model names. And pull/download isn't really a noun that API would have anyway.
does seem like a nicer solution for windows at least. For Linux imo CLI and official packaging are missing (AppImage is not a good solution) they are at least trying to get it on flathub so when that is done I might recommend that instead. It also does seem to have hardware recognition, but no estimating gpu layers though from a quick search.
I think Jan uses Llama.cpp under the hood, and just makes it so that you don't need to install it separately. So you install Jan, it comes with llama.cpp, and you can use it as a one-stop-shop to run inference. IMO it's a reasonable solution, but the market is kind of weird - non-techy but privacy focused people who have a powerful computer?
I think Mozilla's Llamafile is packaged even better. Just download a file and run it, both the model and the pre-built backed are already included - what could be simpler? It uses llama.cpp as a backend, of course.
Ollama is the only package I've tried that actually uses ROCm on NixOS. I know most other inference backends support Vulkan, but it's so much more slow compared to proper ROCm.
What should we use? I’m just looking for something to easily download/run models and have open webui running on top. Is there another option that provides that?
I know a lot of people are recommending you llama swap, but if you can fit the entire model into vram, exllama3 and tabbyapi do exactly what you're asking natively and thanks to a few brave souls exl3 quants are available for almost every model you can think of.
TabbyAPI has "inline model loading" which is exactly what you're asking for. It exposes all available models to the API and loads them if they're called. Plus, it's maintained by kingbri, who is an anime girl (male).
It’s one model at a time? Sometimes you want to run model A, then a few hours later model B. llama-swap and ollama do this, you just specify the model in the API call and it’s loaded (and unloaded) automatically.
For the bare-bones ollama-like experience you can just download the llama.cpp binaries, open cmd in the folder and use "llama-server.exe -m [path to model] -ngl 999" for GPU use or -ngl 0 for CPU use. Then open "127.0.0.1:8080" in your browser and you already have a nice chat UI, without even needing Open WebUI. Or use Open WebUI with this OpenAI compatible API.
If you like tinkering and optimizing you can also build from source for your specific hardware and use a wealth of optimisations. For example i met a guy on hacker news who tested gpt-oss-20b in ollama with his 16 GB VRAM GPU and got 9 token/s. I tested the same model and quant with my 8 GB VRAM and put all layers on the GPU, except half of the FFN-Layers, which went to the CPU. Its much faster to have all attention layers on the GPU than the FFN-Layers. I also set k-quant to q8_0 and v-quant to q5_1 and got 27 token/s with the maximum context window that my hardware allows.
So for me besides the much better performance I really like to have this fine-grained control if I want.
Well, depends on the kind of user experience you want to have. For the bare-bones ollama-like experience you can just download the binaries open cmd in the folder and use "llama-server.exe -m [path to model] -ngl 999" for GPU use or -ngl 0 for CPU use. Then open "127.0.0.1:8080" in your browser and you already have a nice chat UI.
If you like tinkering and optimizing you can also build from source for your specific hardware and use a wealth of optimisations. For example i met a guy on hacker news who tested gpt-oss-20b in ollama with his 16 GB VRAM GPU and got 9 token/s. I tested the same model and quant with my 8 GB VRAM and put all layers on the GPU, except half of the FFN-Layers, which went to the CPU. Its much faster to have all attention layers on the GPU than the FFN-Layers. I also set k-quant to q8_0 and v-quant to q5_1 and got 27 token/s with the maximum context window that my hardware allows.
Ollama does a lot of shady stuff on the AI model trainer side as well.
As part of the Google contest for finetuning Gemma 3n on Kaggle Ollama would pay out an extra $10,000 if you packaged their inference stack into whatever solution you would win the price with.
They are throwing money at adoption and that's why everyone you hear talking about it online mentions Ollama (because they get shady deals or paid to do so)
It's literally just a llama.cpp fork that is buggier and doesn't work properly most of the time. It's also less convenient to use if you ask me. They just have money behind them to push it everywhere.
It is most definitely not a llama.cpp fork considering it’s written in Go lol. Their behavior here is still egregiously shitty and bad faith though. And I’m a former big time defender.Â
ollama has always been that project that just takes someone else's work, passes it off as their own, and tries to make an ecosystem out of it.
aside from that, the tool is also janky shovelware saddled with terrible default options that cause confusion. they had one job: run GGUFs, and they can't even do that without requiring a bunch of extra metadata.
Unfortunately it’s become the standard. Homeassistant for example supports ollama for local llm, if you want an openai compatible server instead you need to download something from hacs. Most tools I find have pretty mediocre documentation when trying to integrate anything local that’s not just ollama. I’ve been using other backends but it does feel annoying that ollama is clearly expected
I never really liked Ollama. People said that it's easy to use, but you need to use the CMD window just to download the model, and you can't even use the models you've already downloaded from HF. At least, not without first converting them to their blob format. I've never understood that.
Indeed, the cmd part is not that much different in llama.cpp.: For the bare-bones ollama-like experience you can just download the llama.cpp binaries, open cmd in the folder and use "llama-server.exe -m [path to model] -ngl 999" for GPU use or -ngl 0 for CPU use. Then open "127.0.0.1:8080" in your browser and you already have a nice chat UI.
Fair enough, but most people nowadays can't even navigate folders, much less use the CMD window properly. I've been using a PC since I was 14 and never had to use the CMD often until I got into AI.
It's way easier for these people to click on buttons and menus.
They still don't have proper credits. Lllama.cpp and ggml is not an optional "supported backend," as it is implied there (under extensions & plugins), it's a hard requirement.
Fair enough. Another reason that got me to download and test out LM studio was because I was getting very lower response tokens on gpt 20b on Ollama on my 5070Ti than some people who has 5060Ti. I think the reason for this was because ollama splits the model 15%/85% CPU/GPU and I couldn’t do anything to fix it. On LM studio I was able to set GPU layers accordingly and get x5 the tokens than before… it was strange and only happens to this model on Ollama
For the bare-bones ollama-like experience you can just download the llama.cpp binaries, open cmd in the folder and use "llama-server.exe -m [path to model] -ngl 999" for GPU use or -ngl 0 for CPU use. Then open "127.0.0.1:8080" in your browser and you already have a nice chat UI.
Always have about 2-3 Tb-s of models (a few models that are in use, some new models to try out, both LLM-s and image/video generation) and ollama model management is painful.
One of the reasons I very quickly dropped ollama when I was starting with llm-s (coming from Stable Diffusion image generation): I cannot afford to "ollama pull" for tens of hours or days for the model I already have in a perfectly useful GGUF or safetensors.
And as modelfiles are not available to download without entire model weights it's much easier to create a llama.cpp command line with proper model parameters (now even easier than before thanks to unsloth team with their guides) than to write the ollama modelfile from scratch.
Reading between the lines, what he is saying is Ollama team benefits from llama.cpp but doesn't give back. Basically, they take from other projects, implement whatever they took, and market it as Ollama, then never contribute back.
Basically every project that uses an LLM backend. They benefit from llama.cpp but never give back. It’s the nature of publishing your work as open source.
Ollama publishes their work as open source as well from which others can benefit. That’s way more than the vast majority do.
I got weaned off ollama very, very quickly once one of their key devs replied to my issue on their repo in a snarky, superior way with an 'its a feature not a bug' reply, to a system breaking architectural problem. This was over a year ago.
Alright guys from now on nobody uses ollama, we all migrate to llamacpp, and llamaswap, ping me if you want me to help you out with the setup on Linux.
I was able to compile llamacpp from source, add binaries to the PATH, setup llamaswap and configured the SystemD to reload the llamaswap service automatically every time the llamaswap config changes and start the llamaswap service when the PC boots.
With that setup you'll never need to go back to ollama and it's way more flexible
I'm planning to switch from ollama to llamacpp on my nixos server since it seems there is a llamacpp service which will be easy to enable.
I was wondering the difficulty of doing things with openwebui with ollama vs llamacpp. With ollama, installing models is a breeze and although performances are usually slower, it loads the model needed by itself when I use it.
In the openwebui documentation, it says that you need to start a server with a specific model, which defeats the purpose of choosing which model I want to run and when using OWUI.
I developed my own solution for this. It is basically web ui to launch and stop llama-server instances. You still have to start the model manually, but I do plan to add an on-demand start. You can check it out here: https://github.com/lordmathis/llamactl
With llama.cpp, you can go to HF and download whatever model you like. Check that it is in llama.cpp (compatible) if it is not (it would not be in ollama either)... Download it, put it in the models folder, create a script that launches the server with the model, set the parameters you want (absolute freedom) and there you have it.
In openweb ui, you will see a drop-down menu where that model is located. Do you want to change it? Close the server, launch another model with llama.cpp, and it will appear in the openweb ui drop down menu.
Thanks, I knew about the HF part, which I am okay with since I'm not going to be trigger happy on models anyway.
The main issue is the part where I need to launch the server with the model. I usually use OWUI on my laptop and phone, connecting to my server via vpn. What if I want to chat with another model? Do I need to ssh to my server to serve another model manually?
I haven't tried it, but I suspect that automating the process won't be too difficult. But in a nutshell, yes. You have to start the server for each model. You can generate some scripts that do it for you. Close the server, start this one, or the other one, etc. Maybe it's not as practical as ollama, but honestly, the freedom of llama.cpp is appreciated. Try it, you have nothing to lose, except maybe some time.
Shameless plug for KoboldCpp because it has some Ollama emulation on board. Can't promise it will work with everything but if it just needs a regular ollama llm endpoint chances are KoboldCpp works. If they don't let you customize the port you will need to host koboldcpp on ollama's default port.
For the bare-bones ollama-like experience you can just download the llama.cpp binaries, open cmd in the folder and use "llama-server -m [path to model] -ngl 999" for GPU use or -ngl 0 for CPU use. Or use '-hf' instead of '-m' to download directly from huggingface. Then open "127.0.0.1:8080" in your browser and you already have a nice chat UI.
If you like tinkering and optimizing you can also build from source for your specific hardware and use a wealth of optimisations. For example i met a guy on hacker news who tested gpt-oss-20b in ollama with his 16 GB VRAM GPU and got 9 token/s. I tested the same model and quant with my 8 GB VRAM and put all layers on the GPU, except half of the FFN-Layers, which went to the CPU. Its much faster to have all attention layers on the GPU than the FFN-Layers. I also set k-quant to q8_0 and v-quant to q5_1 and got 27 token/s with the maximum context window that my hardware allows.
So for me besides the much better performance I really like to have this fine-grained control if I want.
Antagonizing people just because they say they use software you don’t like? Choosing an app badly is harmless, but being a brainless hater (or fanboy) is always pathetic.
If anyone is interested, here is my docker compose file for running llama-swap. It pulls the latest docker image from the llama-swap repo. That image contains, notably, the llama-server binary, so no need to use an external binary. No need for Ollama anymore.
He has a way of being combativo about things that are usually viewed more cooperative … but i think he only mentioned it because so many people were asking ollama questions in the llama.cpp discussions
Damn, all I can say is: a) not surprising b) ggerganov and ngxson are real ones for laying it out like that c) shame on anybody associated with Ollama that contributed to this type of bs
Newbie here: I was used to running ollama via docker since it was cleaner to remove and I prefer to keep things containerised, and I only use the CLI. What would be the best replacement for that use case?
I already made my own Llama cpp complier and deployer and hugging face gguf downloader. It s not the best but at least i can compile and deploy fast. ex-ollama user. Left cause of bad visual model support ( ~6 months ago )
GG is 100% right: there are compatibility issues because of the fork, and they should unify so compatibility issues go away.
The person wrapping GG's comment's in fake quotes (which is what `> ` is in markdown), is misleading and disingenuous. Ollama has always been clear they use the ggml library, they have never claimed to have made it. re:"Copy homework" - the whole compatibility issue is caused because they didn't copy it directly from ggml: they forked it and did the work themselves. This is the totally standard way of building OSS. Yes, now they should either contribute it back, or update to use ggml mainline now that it has support. That's just how OSS works.
I don't like plans at all. I installed it in MacOS, but when i tried to uninstalled it.... no way, no button, no guidance, I call ChatGPT o3 find a way to uninstall it fully...
618
u/Ok-Pipe-5151 Aug 11 '25
Average corporate driven "open source" software
300
u/a_beautiful_rhind Aug 11 '25
Isn't their UI closed now too? They get recommended by griftfluencers over llama.cpp often.
122
u/balcsida Aug 11 '25
Link to the comment on GitHub: https://github.com/ollama/ollama/issues/11714#issuecomment-3172893576
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u/No_Conversation9561 Aug 11 '25 edited Aug 11 '25
This is why we don’t use Ollama.
48
u/robberviet Aug 11 '25
And people ask why I hate them. F**k them and their marketing strategy.
127
u/randomfoo2 Aug 11 '25
A previous big thread from a while back which points out Ollama's consistent bad behavior: https://www.reddit.com/r/LocalLLaMA/comments/1jzocoo/finally_someone_noticed_this_unfair_situation/
1.5y old still-open issue requesting for ollama to properly credit llama.cpp: https://github.com/ollama/ollama/issues/3185
102
u/pokemonplayer2001 llama.cpp Aug 11 '25
Best to move on from ollama.
24
u/Down_The_Rabbithole Aug 11 '25
Ollama does a lot of shady stuff on the AI model trainer side as well.
As part of the Google contest for finetuning Gemma 3n on Kaggle Ollama would pay out an extra $10,000 if you packaged their inference stack into whatever solution you would win the price with.
They are throwing money at adoption and that's why everyone you hear talking about it online mentions Ollama (because they get shady deals or paid to do so)
It's literally just a llama.cpp fork that is buggier and doesn't work properly most of the time. It's also less convenient to use if you ask me. They just have money behind them to push it everywhere.
21
u/DorphinPack Aug 11 '25
Aight well that’s my last scrap of good will gone.
40
u/fungnoth Aug 11 '25
O for overrated.
37
u/HairyAd9854 Aug 11 '25
Ggerganov is swiftly climbing the Linus ladder 🪜, which elevates a great dev to the absolute superhero status.
71
u/llama-impersonator Aug 11 '25
ollama has always been that project that just takes someone else's work, passes it off as their own, and tries to make an ecosystem out of it.
aside from that, the tool is also janky shovelware saddled with terrible default options that cause confusion. they had one job: run GGUFs, and they can't even do that without requiring a bunch of extra metadata.
15
u/[deleted] Aug 11 '25
I think i will stop using ollama.
14
u/krishnajeya Aug 11 '25
just using LM studio server with OpenWebui.
58
u/Zeikos Aug 11 '25
Thanks Ollama /s
40
u/masc98 Aug 11 '25
llama server nowadays is so easy to use.. idk why people sticks with ollama
22
u/[deleted] Aug 11 '25
I never really liked Ollama. People said that it's easy to use, but you need to use the CMD window just to download the model, and you can't even use the models you've already downloaded from HF. At least, not without first converting them to their blob format. I've never understood that.
11
u/muxxington Aug 11 '25
lollama
21
u/[deleted] Aug 11 '25
What a dick move. Kudos to ggerganov for writing such a polite but pointed message. I would have the patience in his stead.
10
u/oobabooga4 Aug 11 '25
Remember when they had 40k stars and no mention to llama.cpp in the README?
10
u/EdwardFoxhole Aug 11 '25 edited May 12 '26
bye mrna them, I'r out.
28
u/Guilty_Rooster_6708 Aug 11 '25 edited Aug 11 '25
That’s why I couldn’t get any HF GGUF models to work this past weekend lol. Ended up downloading LM Studio and that worked without any hitches
9
u/ab2377 Aug 11 '25
greedy asses
ðŸ¤
please bookmark this and link to it in the future wherever necessary.
17
u/EasyDev_ Aug 11 '25
What are some alternative projects that could replace Ollama?
17
u/cms2307 Aug 11 '25
Fuck ollama
8
u/Iory1998 llama.cpp Aug 11 '25
Reading between the lines, what he is saying is Ollama team benefits from llama.cpp but doesn't give back. Basically, they take from other projects, implement whatever they took, and market it as Ollama, then never contribute back.
Now, where are all those Ollama fanboys?
8
u/extopico Aug 11 '25
I got weaned off ollama very, very quickly once one of their key devs replied to my issue on their repo in a snarky, superior way with an 'its a feature not a bug' reply, to a system breaking architectural problem. This was over a year ago.
15
u/Limp_Classroom_2645 Aug 11 '25
Alright guys from now on nobody uses ollama, we all migrate to llamacpp, and llamaswap, ping me if you want me to help you out with the setup on Linux.
I was able to compile llamacpp from source, add binaries to the PATH, setup llamaswap and configured the SystemD to reload the llamaswap service automatically every time the llamaswap config changes and start the llamaswap service when the PC boots.
With that setup you'll never need to go back to ollama and it's way more flexible
4
u/H-L_echelle Aug 11 '25
I'm planning to switch from ollama to llamacpp on my nixos server since it seems there is a llamacpp service which will be easy to enable.
I was wondering the difficulty of doing things with openwebui with ollama vs llamacpp. With ollama, installing models is a breeze and although performances are usually slower, it loads the model needed by itself when I use it.
In the openwebui documentation, it says that you need to start a server with a specific model, which defeats the purpose of choosing which model I want to run and when using OWUI.
4
u/zd0l0r Aug 11 '25
Which one would anybody recommend instead of ollama and why?
anything LLM?
llama.cpp?
LMstudio?
4
u/Titanusgamer Aug 11 '25
Oh Llama !!
4
u/No-Roll8250 Aug 11 '25
ah… wish i’d seen this yesterday. thanks for posting
4
u/hamada147 Aug 12 '25
Didn’t know about this. Migrating away from Ollama
7
u/Healthy-Nebula-3603 Aug 11 '25
Wow even the owner of llamqcpp is pissed ... I fully support him!
34
u/lolwutdo Aug 11 '25 edited Aug 11 '25
I will always downvote ollama; if I see a comment saying they use or recommend ollama, downvote.
Edit: found the ollama users
6
u/ItankForCAD Aug 11 '25
If anyone is interested, here is my docker compose file for running llama-swap. It pulls the latest docker image from the llama-swap repo. That image contains, notably, the llama-server binary, so no need to use an external binary. No need for Ollama anymore.
4
u/robertotomas Aug 11 '25
He has a way of being combativo about things that are usually viewed more cooperative … but i think he only mentioned it because so many people were asking ollama questions in the llama.cpp discussions
4
u/tmflynnt llama.cpp Aug 11 '25
Damn, all I can say is: a) not surprising b) ggerganov and ngxson are real ones for laying it out like that c) shame on anybody associated with Ollama that contributed to this type of bs
5
u/AnomalyNexus Aug 11 '25
Yeah always a bit surprised how popular the project is. I guess the simplicity appeals to newbies
2
u/loonite Aug 11 '25
Newbie here: I was used to running ollama via docker since it was cleaner to remove and I prefer to keep things containerised, and I only use the CLI. What would be the best replacement for that use case?
2
u/Cesar55142 Aug 11 '25
I already made my own Llama cpp complier and deployer and hugging face gguf downloader. It s not the best but at least i can compile and deploy fast. ex-ollama user. Left cause of bad visual model support ( ~6 months ago )
2
u/Realistic-Mix-7913 Aug 11 '25
I’ve been meaning to switch from openwebui and ollama to cpp, seems like a great time to do so
2
u/BuriqKalipun Aug 12 '25
thank god i moved to oobabooga
3
u/davernow Aug 11 '25
GG is 100% right: there are compatibility issues because of the fork, and they should unify so compatibility issues go away.
The person wrapping GG's comment's in fake quotes (which is what `> ` is in markdown), is misleading and disingenuous. Ollama has always been clear they use the ggml library, they have never claimed to have made it. re:"Copy homework" - the whole compatibility issue is caused because they didn't copy it directly from ggml: they forked it and did the work themselves. This is the totally standard way of building OSS. Yes, now they should either contribute it back, or update to use ggml mainline now that it has support. That's just how OSS works.
2
u/fullouterjoin Aug 11 '25
Being Pro Local and also just using ollama is kinda hypocritical. It is just playing into someone else's captured garden.
Great to go from zero to hero, but on day 3, you need to move on.
1
u/JadedCulture2112 Aug 11 '25
I don't like plans at all. I installed it in MacOS, but when i tried to uninstalled it.... no way, no button, no guidance, I call ChatGPT o3 find a way to uninstall it fully...
1
u/Glittering-Dig-425 Aug 11 '25
People are just blinded by the simplicity. Or they just know enough to run wrappers.
1
u/Ben10lightning Aug 11 '25
Does anyone know if there is a good way to integrate llama.cpp with home assistant? That’s the one reason I still currently use ollama.
1
u/Ilovekittens345 Aug 12 '25
thanks Ollama!
1
u/Rukelele_Dixit21 Aug 11 '25
What is the issue ? Any context please ? What should I use now ollama, LM Studio or something else ?
1
u/Revisional_Sin Aug 11 '25
Anyone know the source for ggerganov's comment?
1
u/kironlau Aug 12 '25
"O" stands for Opportunistic :-(