this post was submitted on 24 Jun 2026
137 points (81.9% liked)
Selfhosted
60177 readers
511 users here now
A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don't control.
Rules:
-
Be civil.
-
No spam.
-
Posts are to be related to self-hosting.
-
Don't duplicate the full text of your blog or readme if you're providing a link.
-
Submission headline should match the article title.
-
No trolling.
-
Promotion posts require active participation, with an account that is at least 30 days old. F/LOSS without a paywall has exceptions, with requirements. See the rules link for details.
Resources:
- selfh.st Newsletter and index of selfhosted software and apps
- awesome-selfhosted software
- awesome-sysadmin resources
- Self-Hosted Podcast from Jupiter Broadcasting
Any issues on the community? Report it using the report flag.
Questions? DM the mods!
founded 3 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
An aside for anyone reading this:
https://sleepingrobots.com/dreams/stop-using-ollama/
And that barely scratches the surface. Please.
Use anything but Ollama. Even APIs.
Didn't know this. Going to switch this weekend, thanks for sharing this!
Thanks for this link. Because of this article, I had claude stand up a llama.cpp container next to my already running ollama container. It ran side by side tests with the same model and parameters, and the results blew ollama out of the water. I'm in the process of moving hermes and openwebgui over to the llama.cpp instance to see how it goes day to day.
If you’re using docker anyway, and “fast” pure GPU models, you might try a vllm container while you’re at it.
It should be much faster than even llama.cpp, albeit at the cost of context length, and it supports some exotic 4-bit quantization like SPQA.
Same with TabbyAPI. It’s quantization is SOTA, though it does not support CPU offloading, and it’s speed is somewhere between vllm and llama.cpp.
Thanks! I'll look into this. I'm a bit limited at 12GB of VRAM right now.
A 3060?
Exllama/TabbyAPI is still worth looking at if you are trying to run a model purely in GPU RAM. It’s easily the most VRAM efficient backend, it just doesn’t support CPU offloading (which is useful for MoEs if you have considerable spare CPU RAM) and more optimized for 4xxx and up Nvidia cards.
And TabbyAPI has a docker container you can use. Look for “exl3” models on huggingface.
thank you
Llama.cpp or death!
It's not that hard to use
llama.cppdirectly anyway. Why would I use a wrapper when I can just run a python script?I use LMStudio, because it has quality of life improvements like nice GUI and huggingface search engine. Also they have Vulkan backend that at least on 7900XTX is ~10% faster than rocm (on LLama 3 8b Q4_0 it gets 115Tokens/s vs 105 on rocm)
Or exllama! Vllm, sglang, Lorax. Koboldcpp, Aphrodite, text-generation-webui, LM Studio, powerinfer, ktransformers, mlc-LLM, really whatever floats your boat. Just not ollama, specifically.
I agree that the concerns listed there are smells, and I wasn't aware of some of the options listed there.
Thank you for sharing this!
looks like extreme nitpicking without any real issues beyond some VC funding a FOSS issues.
//whyre you spamming the comment to everyone? its quite alarmist actually
I completely disagree.
Frankly, I find the description "VC funding a FOSS" offensive. They aren't funding the engine. I've been messing with LLM inference engines since 2022, and Ollama is the worst I've seen in the community.
They misname models for SEO. They leech off llama.cpp while deliberately hiding attribution yet redirecting GH support requests there. They sometimes make their own GGUFs+forked releases which are broken and incompatibile with upstream llama.cpp, just so they can get a release out a day ahead for hype, even though it doesn't really work and they'll never upstream one line. They set a default context size thats basically unusable, they screw up chat templates and deep internal code with no obvious indicators, they release suboptimal quants without iMatrix, they gate you into their internal quantization repo and model card format, they hide model downloads on your hard drive, they mess with standard APIs for no good reason other than to mess up other backends. I could go on and on.
And if that's all fine, they're enshittifying the app with closed code, and pointers to cloud models.
They GIVE LLM inference a bad name, by making it a terrible quality engine that happens to show up in search as the "default." Hence the comments below of people being unimpressed with local inference. And they sap attention from actual llama.cpp devs, without contributing a single dime. Everyone in the localllama communtity hates their guts, and that's not even getting into the interpersonal drama they've stirred.
They are a leech that's a net drag to the whole community, that we can't get rid of because they're attention grifters. And they've gotten worse and worse over time.
It's more morale to use any cloud API over Ollama, in my eyes. They're a grift.
EDIT: And, to be clear, I’m not against VC funded downstream stuff.
LM Studio is good! Even though it’s closed source.
Tons of downstream projects are great.