5 ms·

There is virtually no reason to use Ollama over LM Studio or the myriad of other alternatives. Ollama is slower and they started out as a shameless llama.cpp r

by redrove 6mo ago

There is virtually no reason to use Ollama over LM Studio or the myriad of other alternatives. Ollama is slower and they started out as a shameless llama.cpp ripoff without giving credit and now they “ported” it to Go which means they’re just vibe code translating llama.cpp, bugs included.

iLoveOncall 6mo ago
> There is virtually no reason to use Ollama over LM Studio or the myriad of other alternatives. Hmm, the fact that Ollama is open-source, can run in Docker, etc.?
- DiabloD3 6mo ago
  Ollama is quasi-open source. In some places in the source code they claim sole ownership of the code, when it is highly derivative of that in llama.cpp (having started its life as a llama.cpp frontend). They keep it the same license, however, MIT. There is no reason to use Ollama as an alternative to llama.cpp, just use the real thing instead.
  - simondotau 6mo ago
    If it’s MIT code derived from MIT code, in what way is its openness ”quasi”? Issues of attribution and crediting diminish the karma of the derived project, but I don’t see how it diminishes the level of openness.
    - DiabloD3 6mo ago
      FOSS licensing can only exist in terms of Copyright. Without Copyright, you cannot license FOSS. If something has an incorrect Copyright attribution, then the license can be viewed as invalid until this deficiency has been corrected (obv. depending on local laws, etc). On top of this, it would not be unreasonable for the numerous authors of llama.cpp to issue DMCA takedown requests if Ollama is unwilling to correct it.
alifeinbinary 6mo ago
I really like LM Studio when I can use it under Windows but for people like me with Intel Macs + AMD gpu ollama is the only option because it can leverage the gpu using MoltenVK aka Vulkan, unofficially. We're still testing it, hoping to get the Vulkan support in the main branch soon. It works perfectly for single GPUs but some edge cases when using multiple GPUs are unsupported until upstream support from MoltenVK comes through. But yeah, I agree, it wasn't cool to repackage Georgi's work like that.
lousken 6mo ago
lm studio is not opensource and you can't use it on the server and connect clients to it?
- jedisct1 6mo ago
  LM Studio can absolutely run as as server.
  - walthamstow 6mo ago
    IIRC it does so as default too. I have loads of stuff pointing at LM Studio on localhost
meltyness 6mo ago
I feel like the READMEs for these 3 large popular packages already illustrate tradeoffs better than hacker news argument
gen6acd60af 6mo ago
LM Studio is closed source. And didn't Ollama independently ship a vision pipeline for some multimodal models months before llama.cpp supported it?
- zozbot234 6mo ago
  Yes, they introduced that Golang rewrite precisely to support the visual pipeline and other things that weren't in llama.cpp at the time. But then llama.cpp usually catches up and Ollama is just left stranded with something that's not fully competitive. Right now it seems to have messed up mmap support which stops it from properly streaming model weights from storage when doing inference on CPU with limited RAM, even as faster PCIe 5.0 SSDs are finally making this more practical. The project is just a bit underwhelming overall, it would be way better if they just focused on polishing good UX and fine-tuning, starting from a reasonably up-to-date version of what llama.cpp provides already.
faitswulff 6mo ago
Does LM Studio have an equivalent to the ollama launch command? i.e. `ollama launch claude --model qwen3.5:35b-a3b-coding-nvfp4`
- DiabloD3 6mo ago
  I don't think it does, but llama.cpp does, and can load models off HuggingFace directly (so, not limited to ollama's unofficial model mirror like ollama is). There is no reason to ever use ollama.
  - ffsm8 6mo ago
    > I don't think it does, but llama.cpp does I just checked their docs and can't see anything like it. Did you mistake the command to just download and load the model?
    - u8080 6mo ago
      -hf ModelName:Q4_K_M
      - ffsm8 6mo ago
        Did you mistake the command to just download and load the model too? Actually that shouldn't be a question, you clearly did. Hint: it also opens Claude code configured to use that model
    - DiabloD3 6mo ago
      As a sibling comment answered you, it is `-hf`. And yes, it downloads the model, caches it, and then serves future loads of that model out of the cache if the file hasn't changed in the hf repo.
      - ffsm8 6mo ago
        So I'm summary: no, it does not have an equivalent command either.
logicallee 6mo ago
>Ollama is slower I've benchmarked this on an actual Mac Mini M4 with 24 GB of RAM, and averaged 24.4 t/s on Ollama and 19.45 t/s on LM Studio for the same ~10 GB model (gemma4:e4b), a difference which was repeated across three runs and with both models warmed up beforehand. Unless there is an error in my methodology, which is easy to repeat[1], it means Ollama is a full 25% faster. That's an enormous difference. Try it for yourself before making such claims. [1] script at: https://pastebin.com/EwcRqLUm https://pastebin.com/EwcRqLUm but it warms up both and keeps them in memory, so you'll want to close almost all other applications first. Install both ollama and LM Studio and download the models, change the path to where you installed the model. Interestingly I had to go through 3 different AI's to write this script: ChatGPT (on which I'm a Pro subscriber) thought about doing so then returned nothing (shenanigans since I was benchmarking a competitor?), I had run out of my weekly session limit on Pro Max 20x credits on Claude (wonder why I need a local coding agent!) and then Google rose to the challenge and wrote the benchmark for me. I didn't try writing a benchmark like this locally, I'll try that next and report back.
- dminik 6mo ago
  It depends on the hardware, backend and options. I've recently tried running some local AIs (Qwen3.5 9B for the numbers here) on an older AMD 8GB VRAM GPU (so vulkan) and found that: llama.cpp is about 10% faster than LM studio with the same options. LM studio is 3x faster than ollama with the same options (~13t/s vs ~38t/s), but messes up tool calls. Ollama ended up slowest on the 9B, Queen3.5 35B and some random other 8B model. Note that this isn't some rigorous study or performance benchmarking. I just found ollama unnaceptably slow and wanted to try out the other options.
jrm4 6mo ago
Do y'all mean backend or the Ollama frontend or both? I find it trivially easy to sub in my local Ollama api thing in virtually all of the interesting frontend things. I'm quite curious about the "why not Ollama" here.

by redrove 6mo ago

iLoveOncall 6mo ago

> There is virtually no reason to use Ollama over LM Studio or the myriad of other alternatives. Hmm, the fact that Ollama is open-source, can run in Docker, etc.?

DiabloD3 6mo ago
Ollama is quasi-open source. In some places in the source code they claim sole ownership of the code, when it is highly derivative of that in llama.cpp (having started its life as a llama.cpp frontend). They keep it the same license, however, MIT. There is no reason to use Ollama as an alternative to llama.cpp, just use the real thing instead.
- simondotau 6mo ago
  If it’s MIT code derived from MIT code, in what way is its openness ”quasi”? Issues of attribution and crediting diminish the karma of the derived project, but I don’t see how it diminishes the level of openness.
  - DiabloD3 6mo ago
    FOSS licensing can only exist in terms of Copyright. Without Copyright, you cannot license FOSS. If something has an incorrect Copyright attribution, then the license can be viewed as invalid until this deficiency has been corrected (obv. depending on local laws, etc). On top of this, it would not be unreasonable for the numerous authors of llama.cpp to issue DMCA takedown requests if Ollama is unwilling to correct it.

alifeinbinary 6mo ago

I really like LM Studio when I can use it under Windows but for people like me with Intel Macs + AMD gpu ollama is the only option because it can leverage the gpu using MoltenVK aka Vulkan, unofficially. We're still testing it, hoping to get the Vulkan support in the main branch soon. It works perfectly for single GPUs but some edge cases when using multiple GPUs are unsupported until upstream support from MoltenVK comes through. But yeah, I agree, it wasn't cool to repackage Georgi's work like that.

lousken 6mo ago

lm studio is not opensource and you can't use it on the server and connect clients to it?

jedisct1 6mo ago
LM Studio can absolutely run as as server.
- walthamstow 6mo ago
  IIRC it does so as default too. I have loads of stuff pointing at LM Studio on localhost

meltyness 6mo ago

I feel like the READMEs for these 3 large popular packages already illustrate tradeoffs better than hacker news argument

gen6acd60af 6mo ago

LM Studio is closed source. And didn't Ollama independently ship a vision pipeline for some multimodal models months before llama.cpp supported it?

zozbot234 6mo ago
Yes, they introduced that Golang rewrite precisely to support the visual pipeline and other things that weren't in llama.cpp at the time. But then llama.cpp usually catches up and Ollama is just left stranded with something that's not fully competitive. Right now it seems to have messed up mmap support which stops it from properly streaming model weights from storage when doing inference on CPU with limited RAM, even as faster PCIe 5.0 SSDs are finally making this more practical. The project is just a bit underwhelming overall, it would be way better if they just focused on polishing good UX and fine-tuning, starting from a reasonably up-to-date version of what llama.cpp provides already.

faitswulff 6mo ago

Does LM Studio have an equivalent to the ollama launch command? i.e. `ollama launch claude --model qwen3.5:35b-a3b-coding-nvfp4`

DiabloD3 6mo ago
I don't think it does, but llama.cpp does, and can load models off HuggingFace directly (so, not limited to ollama's unofficial model mirror like ollama is). There is no reason to ever use ollama.
- ffsm8 6mo ago
  > I don't think it does, but llama.cpp does I just checked their docs and can't see anything like it. Did you mistake the command to just download and load the model?
  - u8080 6mo ago
    -hf ModelName:Q4_K_M
    - ffsm8 6mo ago
      Did you mistake the command to just download and load the model too? Actually that shouldn't be a question, you clearly did. Hint: it also opens Claude code configured to use that model
  - DiabloD3 6mo ago
    As a sibling comment answered you, it is `-hf`. And yes, it downloads the model, caches it, and then serves future loads of that model out of the cache if the file hasn't changed in the hf repo.
    - ffsm8 6mo ago
      So I'm summary: no, it does not have an equivalent command either.

logicallee 6mo ago

>Ollama is slower I've benchmarked this on an actual Mac Mini M4 with 24 GB of RAM, and averaged 24.4 t/s on Ollama and 19.45 t/s on LM Studio for the same ~10 GB model (gemma4:e4b), a difference which was repeated across three runs and with both models warmed up beforehand. Unless there is an error in my methodology, which is easy to repeat[1], it means Ollama is a full 25% faster. That's an enormous difference. Try it for yourself before making such claims. [1] script at: https://pastebin.com/EwcRqLUm https://pastebin.com/EwcRqLUm but it warms up both and keeps them in memory, so you'll want to close almost all other applications first. Install both ollama and LM Studio and download the models, change the path to where you installed the model. Interestingly I had to go through 3 different AI's to write this script: ChatGPT (on which I'm a Pro subscriber) thought about doing so then returned nothing (shenanigans since I was benchmarking a competitor?), I had run out of my weekly session limit on Pro Max 20x credits on Claude (wonder why I need a local coding agent!) and then Google rose to the challenge and wrote the benchmark for me. I didn't try writing a benchmark like this locally, I'll try that next and report back.

dminik 6mo ago
It depends on the hardware, backend and options. I've recently tried running some local AIs (Qwen3.5 9B for the numbers here) on an older AMD 8GB VRAM GPU (so vulkan) and found that: llama.cpp is about 10% faster than LM studio with the same options. LM studio is 3x faster than ollama with the same options (~13t/s vs ~38t/s), but messes up tool calls. Ollama ended up slowest on the 9B, Queen3.5 35B and some random other 8B model. Note that this isn't some rigorous study or performance benchmarking. I just found ollama unnaceptably slow and wanted to try out the other options.

jrm4 6mo ago

Do y'all mean backend or the Ollama frontend or both? I find it trivially easy to sub in my local Ollama api thing in virtually all of the interesting frontend things. I'm quite curious about the "why not Ollama" here.