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I think this is a game changer, because data privacy is a legitimate concern for many enterprise users. Btw, you can also run Mistral locally within the Docker model runner on a Mac.
Mistral really became what all the other over-hyped EU AI start-ups / collectives (Stability, Eleuther, Aleph Alpha, Nyonic, possibly Black Forest Labs, government-funded collaborations, ...) failed to achieve, although many of them existed way before Mistral. Congrats to them, great work.
One can deploy similar solution (on-prem) using better and more cost efficient open-source models and infrastructure already.
What Mistral offers here is managing that deployment for you, but there's nothing stopping other companies doing the same with fully open stack. And those will have the benefit of not wasting money on R&D.
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Same here. Since I started using LLMs a bit more, the killer step for me was to set up API access to a variety of providers (Mistral, Anthropic, Gemini, OpenAI), and use a unified client to access them. I'm usually coding at the CLI, so I installed 'aichat' from github and it does an amazing job. Switch models on the fly, switch between one-shot and session mode, log everything locally for later access, and ask casua…
Gemini's free tier will absolutely use your inputs for training [1], same with Mistral's free tier [2]. Anthropic and OpenAI let's you opt into data collection for discounted prices or free tokens. [1]: https://ai.google.dev/gemini-api/terms#data-use-unpaid [2]: https://mistral.ai/terms#privacy-policy
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Strange idea, but if I'd like to set up a solid LLM for use in my home network, how much processing power would I need for a multi-purpose model? A Raspberry Pi? And old ThinkPad? A fully speced-out latest gen Macbook? edit: One of those old Mac Pros?
That’s what I tried initially, an old black tin can Mac Pro, but it couldn’t do it. Next splashed on an m2 ultra 64gb mpro, runs ollama with qwen3 32b - reverse shell into the localhost with open web-ui and automatic111 and voila AI on my home network
I think this is a game changer, because data privacy is a legitimate concern for many enterprise users. Btw, you can also run Mistral locally within the Docker model runner on a Mac.
> I think this is a game changer, because data privacy is a legitimate concern for many enterprise users. Indeed. At work, we are experimenting with this. Using a cloud platform is a non-starter for data confidentiality reasons. On-premise is the way to go. Also, they’re not American, which helps. > Btw, you can also run Mistral locally within the Docker model runner on a Mac. True, but you can do that only with thei…
Unless you have experience hosting and maintaining models at scale and with an enterprise feature set, then I believe what they are offering is beyond (for now) what you’d be able put up on your own.
Mistral really became what all the other over-hyped EU AI start-ups / collectives (Stability, Eleuther, Aleph Alpha, Nyonic, possibly Black Forest Labs, government-funded collaborations, ...) failed to achieve, although many of them existed way before Mistral. Congrats to them, great work.
I think this is a game changer, because data privacy is a legitimate concern for many enterprise users. Btw, you can also run Mistral locally within the Docker model runner on a Mac.
Game changer feels a bit strong. This is a new entry in a field that's already pretty crowded with open source tooling that's already available to anyone with the time and desire to wire it all up. It's likely that they execute this better than the community-run projects have so far and make it more approachable and Enterprise friendly, but just for reference I have most of the features that they've listed here alrea…
Those who don't have the time and desire to wire it all up probably make up a larger part of the market than those who do. It's a long-tail proposition, and that might be a problem.
> I have most of the features that they've listed here already set up on my desktop at home
I think your boss and your boss' boss are the audience they are going for. In my org there's concern over the democratization of locally run LLMs and the loss of data control that comes with it.
Mistral's product would allow IT or Ops or whatever department to set guardrails for the organization. The selling point that it's turn-key means that a small organization doesn't have to invest a ton of time into all the tooling needed to run it and maintain it.
Edit: I just re-read your comment and I do have to agree though. "game-changer" is a bit strong of a word.
Mistral really became what all the other over-hyped EU AI start-ups / collectives (Stability, Eleuther, Aleph Alpha, Nyonic, possibly Black Forest Labs, government-funded collaborations, ...) failed to achieve, although many of them existed way before Mistral. Congrats to them, great work.
Mistral models though are not interesting as models. Context handling is weak, language is dry, coding mediocre; not sure why would anyone chose it over Chinese (Qwen, GLM, Deepseek) or American models (Gemma, Command A, Llama).
I flip back and forth with Claude and Le Chat and find them comparable. Le Chat always feels very quick and concise. That's just vibes not benchmarks.
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I'm guessing it's flexible. Mistral makes small models capable of running on consumer hardware so they can probably scale up and down based on needs. And what is available from hosts.
I run a Mistral model on my phone!