I'm also interested to see if that small minority of people are willing to pay for a service like this.
I looked up the Ollama trademark and was surprised to see it's a Canadian company.
He (almost) single-handedly brought LLMs to the masses.
With the latest news of some AI engineers' compensation reaching up to a billion dollars, feels a bit unfair that Georgi is not getting a much larger slice of the pie.
I think he's happy doing his own thing.
But then, if someone came in with a billion ... who wouldn't give it a thought?
$50M, now thats just perfect. you're retired, nor burdened with a huge responsibility
> ggml.ai is a company founded by Georgi Gerganov to support the development of ggml. Nat Friedman and Daniel Gross provided the pre-seed funding.
Now I am going to go and write a wrapper around llamacpp, that is only open source, truly local.
How can I trust ollama to not to sell my data.
You don't need to use Turbo mode; it's just there for people who don't have capable enough GPUs.
- Speed
- Cost
- Reliability
- Feature Parity (eg: context caching)
- Performance (What quant level is being used...really?)
- Host region/data privacy guarantees
- LTS
And that's not even including the decision of what model you want to use!
Realistically if you want to use an OSS model instead of the big 3, you're faced with evalutating models/providers across all these axes, which can require a fair amount of expertise to discern. You may even have to write your own custom evaluations. Meanwhile Anthropic/OAI/Google "just work" and you get what it says on the tin, to the best of their ability. Even if they're more expensive (and they're not that much more expensive), you are basically paying for the priviledge of "we'll handle everything for you".
I think until providers start standardizing OSS offerings, we're going to continue to exist in this in-between world where OSS models theoretically are at performance parity with closed source, but in practice aren't really even in the running for serious large scale deployments.
[1] https://californiarecorder.com/sam-altman-requires-ai-privil...
> The order, embedded under and issued on Might 13, 2025, by U.S. Justice of the Peace Decide Ona T. Wang
Is this some meme where “may” is being replaced with “might”, or some word substitution gone awry? I don’t get it.
If I use local/OSS models it's specifically to avoid running in a country with no data protection laws. It's a big close miss here.
All things considered though, Europe is getting confusing. They have GDPR but now pushing to backdoor encryption within the EU? [1]
At least there isn't a strong movement in the US trying to outlaw E2E encryption.
[1] https://www.eff.org/deeplinks/2025/06/eus-encryption-roadmap...
Which brings up the point are truly private LLMs possible? Where the input I provide is only meaningful to me, but the LLM can still transform it without gaining any contextual value out of it? Without sharing a key? If this can be done, can it be done performantly?
Yes, there is gonna be a new discussion for it on October 15, but I've already seen section of governments being against their own government position on the bill (Swedish Military for example).
I guarantee that nobody cares about or will be surveilling your private AI use unless you're doing other things that warrant surveillance.
The reason big providers suck, as OpenAI is so nicely demonstrating for us, is that they retain everything, the user is the product, and court cases, other situations can unmask and expose everything you do on a platform to third parties. This country seriously needs a digital bill of rights.
The biggest game in town has been managing platforms that give owners an information advantage. But at least the world generally trusts the USA to abide by laws and user agreements, which is why, to my mind, the USA retains the near monopoly on information platforms.
I personally wouldn’t trust a UK platform for example, being a Brit native. The top echelon talent pool is so small and incestuous I don’t believe I would experience a fair playing field if a business of mine passed a certain size of national reach/importance.
EDIT: from ChatGPT, new money entrepreneurs with no inheritence/political ties by economic region, USA ~63%, UK/HongKong/Singapore ~45%, Emerging Markets ~35%, EU ~22%, Russia ~10%
https://www.anthropic.com/pricing - $0 / $17 (if billed annually) / $20 (if billed monthly) / $100 / $25 (team) / custom enterprise pricing / on-demand API pricing
Sounds like tiers to me.
Thankfully, this may just leave more room for other open source local inference engines.
This isn't Anaconda, they didn't do a bait and switch to screw their core users. It isn't sinful for devs to try and earn a living.
If you earn a living using something someone else built, and expect them not to earn a living, your paycheck has a limited lifetime.
“Someone” in this context could be a person, a team, or a corporate entity. Free may be temporary.
It was always just a wrapper around the real well designed OSS, llama.cpp. Ollama even messes up the names of models by calling distilled models the name of the actual one, such as DeepSeek.
Ollama's engineers created Docker Desktop, and you can see how that turned out, so I don't have much faith in them to continue to stay open given what a rugpull Docker Desktop became.
There are areas we will make money, and I wholly believe if we follow our conscious we can create something amazing for the world while making sure we can keep it fueled to keep it going for the long term.
Some of the ideas in Turbo mode (completely optional) is to serve the users who want a faster GPU, and adding in additional capabilities like web search. We loved the experience so much that we decided to give web search to non-paid users too. (Again, it's fully optional). Now to prevent abuse and make sure our costs don't go out of hand, we require login.
Can't we all just work together and create a better world? Or does it have to be so zero sum?
For Turbo mode I understand the need for paying but the main poing of running a local model with web search is browsing from my computer without using any LLM provider. Also I want to get rid of the latency to US servers from Europe.
If ollama can't do it, maybe a fork.
Wait until it makes significant amounts of money. Suddenly the priorities will be different.
I don’t begrudge them wanting to make some money off it though.
I'm not sure which package we use that is triggering this. My guess is llama.cpp based on what I see on social? Ollama has long shifted to using our own engine. We do use llama.cpp for legacy and backwards compatibility. I want to be clear it's not a knock on the llama.cpp project either.
There are certain features we want to build into Ollama, and we want to be opinionated on the experience we want to build.
Have you supported our past gigs before? Why not be more happy and optimistic in seeing everyone build their dreams (success or not).
If you go build a project of your dreams, I'd be supportive of it too.
Developers continue to be blind to usability and UI/UX. Ollama lets you just install it, just install models, and go. The only other thing really like that is LM-Studio.
It's not surprising that the people behind it are Docker people. Yes you can do everything Docker does with Linux kernel and shell commands, but do you want to?
Making software usable is often many orders of magnitude more work than making software work.
No inference engine does all of:
- Model switching
- Unload after idle
- Dynamic layer offload to CPU to avoid OOM
All companies that raise outside investment follow this route.
No exceptions.
And yes this is how ollama will fall due to enshittification, for lack of a better word.
if i could have consistent and seamless local-cloud dev that would be a nice win. everyone has to write things 3x over these days depending on your garden of choice, even with langchain/llamaindex
Sure, llama.cpp is the real thing, ollama is a wrapper... I would never want to use something like ollama in a production setting. But if I want to quickly get someone less technical up to speed to develop an LLM-enabled system and run qwen or w/e locally, well then its pretty nice that they have a GUI and a .dmg to install.
Since the new multimodal engine, Ollama has moved off of llama.cpp as a wrapper. We do continue to use the GGML library, and ask hardware partners to help optimize it.
Ollama might look like a toy and what looks trivial to build. I can say, to keep its simplicity, we go through a deep amount of struggles to make it work with the experience we want.
Simplicity is often overlooked, but we want to build the world we want to see.
I knew a startup that deployed ollama on a customers premises and when I asked them why, they had absolutely no good reason. Likely they did it because it was easy. That's not the "easy to use" case you want to solve for.
We benchmarked vLLM and Ollama on both startup time and tokens per seconds. Ollama comes at the top. We hope to be able to publish these results soon.
For Draw Things provided "Cloud Compute", we don't retain any data too (everything is done in RAM per request). But that is still unsatisfactory personally. We will soon add "privacy pass" support, but still not to the satisfactory. Transparency log that can be attested on the hardware would be nice (since we run our open-source gRPCServerCLI too), but I just don't know where to start.
[full disclosure I am working on something with actual privacy guarantees for LLM calls that does use a transparency log, etc.]
It is completely compromised, especially if it is an AI company.
How do you think ollama was able to provide the open source AI models to everyone for free?
I am pretty sure ollama was losing money on every pull of those images from their infrastructure.
Those that are now angry at ollama charging money or not focusing on privacy should have been angry when they raised money from investors.
https://github.com/ollama/ollama/issues/5245
If any of the major inference engines - vLLM, Sglang, llama.cpp - incorporated api driven model switching, automatic model unload after idle and automatic CPU layer offloading to avoid OOM it would avoid the need for ollama.
However the approach to model swapping is not 'ollama compatible' which means all the OSS tools supporting 'ollama' Ex Openwebui, Openhands, Bolt.diy, n8n, flowise, browser-use etc.. aren't able to take advantage of this particularly useful capability as best I can tell.
This allows you to try out some open models and better assess if you could buy a dgx box or Mac Studio with a lot of unified memory and build out what you want to do locally without actually investing in very expensive hardware.
Certain applications require good privacy control and on-prem and local are something certain financial/medical/law developers want. This allows you to build something and test it on non-private data and then drop in real local hardware later in the process.
I pay $20 to Anthropic, so I don’t think I’d get enough use out of this for the $20 fee. But being able to spin up any of these models and use as needed (and compare) seems extremely useful to me.
I hope this works out well for the team.
Agreed, though there are already several providers of these new OpenAI models available, so I'm not sure what ollama's value add is there (there are plenty of good chat/code/etc interfaces available if you are bringing your own API keys).
In a universe where everything you say can be taken out of context, things like OpenAi will be a data leak nightmare.
Need this soon:
It's very unfortunate that the local inference community has aggregated around Ollama when it's clear that's not their long term priority or strategy.
Its imperative we move away ASAP
Is it bad to fairly charge money for selling GPUs that cost us money too, and use that money to grow the core open-source project?
At one point, it just has to be reasonable. I'd like to believe by having a conscientious, we can create something great.
I moved away from ollama in favor of llama-server a couple of months ago and never missed anything, since I'm still using the same UI.
Assuming you have llama-server installed, you can download + run a hugging face model with something like
llama-server -hf ggml-org/gpt-oss-20b-GGUF -c 0 -fa --jinja
Ollama does not use llama.cpp anymore; we do still keep it and occasionally update it to remain compatible for older models for when we used it. The team is great, we just have features we want to build, and want to implement the models directly in Ollama. (We do use GGML and ask partners to help it. This is a project that also powers llama.cpp and is maintained by that same team)
That is interesting, did Ollama develop its own proprietary inference engine or did you move to something else?
Any specific reason why you moved away from llama.cpp?
> We do use GGML
Sorry, but this is kind of hiding the ball. You don't use llama.cpp, you just ... use their core library that implements all the difficult bits, and carry a patchset on top of it?
Why do you have to start with the first statement at all? "we use the core library from llama.cpp/ggml and implement what we think is a better interface and UX. we hope you like it and find it useful."
Why? If the tool works then use it. They’re not forcing you to use the cloud.
For the users who want GPUs, which cost us money, we will charge money for it. Completely optional.
For one of the top local open model inference engines of choice - only supporting OSS out of the gate feels like an angle to just ride the hype knowing OSS is announced today "oh OSS came out and you can use Ollama Turbo to use it"
The subscription based pricing is really interesting. Other players offer this but not for API type services. I always imagine that there will be a real pricing war with LLMs with time / as capabilities mature, and going monthly pricing on API services is possibly a symptom of that
What does this mean for the local inference engine? Does Ollama have enough resources to maintain both?
I guess their target audience values convenience and easy of use above all else so that could play well there maybe.
Doesn't look that much better than a ChatGPT Plus subscription.
> Turbo is a new way to run open models using datacenter-grade hardware.
What? Why not just say that it is a cloud-based service for running models? Why this language?
turnsout•5h ago
sambaumann•5h ago
> OpenAI and Ollama partner to launch gpt-oss