Do you mean by trading dollars for the privacy you need as:

a) Contracting with a third-party independent inference provider who will run your choice of model on fast hardware that they own, with all appropriate data security/privacy/contractual/compliance protection in place

b) Contracting with the original creators of the model to run inference via their API and with assurances that all the same data protection is in place

c) Spending the money to buy your own inference hardware to run it on something you fully own/control at proper usable speeds?

Edit: Everything I've been writing in this thread is mostly within the context of being able to evaluate K3 and its usefulness to be self-hosted as a preliminary proof of concept or test of feasibility of a new thing, such as on <$20,000 of server hardware, before proceeding to spend 300-400k on GPU-related hardware, or external third party services/ongoing billing.

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30 minutes ago | parent | next [-]

[deleted]

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jmalicki 4 hours ago | parent | prev [-]

A) is very doable with e.g. Amazon Bedrock.

They'll give you HIPAA compliance, they even have a data center for US government classified data, they can give you European data sovereignty. And with OpenAI and Anthropic models to boot, you don't even have to settle for open weights.

What kind of privacy needs do you really have beyond that?

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walrus01 4 hours ago | parent [-]

It is not my use case but given recent political developments in international relations caused by the executive branch of the US government, off the top of my head, I could think of a lot of European or Canadian firms for which that would not be an option. No matter what they might promise about European sovereignty. For a good 'ol patriotic US domestic company? Sure.

	▲	Taunt4 3 hours ago \| parent [-]
		Yes, its the US cloud act risk EU companies run up against on hyperscalers like MS/AWS. Even for EU companies running open weights on EU stacks LLM inference on the GPU must process plaintext and I can't find any EU provider with NVIDIA H100/H200/Blackwell CC mode plus SEV-SNP or TDX, where you can cryptographically verify the workload ran somewhere the operator cannot inspect. Personal compute is therefore the only option if you want personal autonomy privacy for IP &c. Maybe another option is to use cloud compute rented to fine tune a personal model that suits your own needs that would help bring the cost down, I don't know enough about this area to know if it kills the "intelligence" of those domains due to limited ?cross-verification within the LLM.