The fundamental difference is access and control. Proprietary models are like a "black box" service you call over the internet, where the inner workings are kept secret by the company that owns it. Open-source models can be downloaded, inspected, modified, and run on your own servers.

Proprietary models run in the provider's cloud environment (e.g., OpenAI's or Google's servers). Open-source models can be run in your own environment, either on-premises or in your private cloud account.

With proprietary models, your data is sent to the provider's servers to be processed. This requires trusting the provider to handle your data according to their terms. With open-source models, your data stays within your own environment, giving you complete control.

Proprietary models offer limited customization. In contrast, open-source models are highly customizable. You can download them and fine-tune them with your own data to specialize their performance for specific tasks.

• Proprietary Models: GPT-4 (from OpenAI), Claude 3 (from Anthropic), and Gemini (from Google).

• Open-Source Models: Llama 3 (from Meta), Mistral (from Mistral AI), and Falcon (from TII).

Generally, the top proprietary models are considered to have the highest performance and quality of output. Open-source models are improving very quickly but often lag slightly behind the state-of-the-art proprietary versions. However, an open-source model may be "good enough" for many specific use cases.

A separate device has the RAM/CPUA and HDD needed to run an LLM. A separate device have advantage of having everything setup - connect to your wi-fi and start working. It does not consume resources of your main work device - no lags, no installation issues. The third advantage is that it is easy to share between team members.

EPrivify is a solution designed to provide a private Large Language Model experience. Unlike many existing LLMs that collect and use your data for training, EPrivify runs in a secure cloud environment, giving users full control over their data and ensuring privacy. It aims to be an accessible and affordable alternative to mainstream LLMs.

EPrivify ensures data privacy by operating in a private cloud environment where users maintain complete control over their data. The platform does not use your interactions or data for its own training purposes, and it offers granular control over settings and agent capabilities to further protect your information.

Users of EPrivify have extensive control over their experience. This includes full control over their data, as well as the ability to manage settings and define what AI agents can and cannot do. This empowers users to tailor the AI to their specific needs and privacy preferences.

EPrivify is envisioned as a foundational platform for building a new operating system. While it doesn't directly replace traditional operating systems like Windows or macOS, it aims to serve as a core component for a new digital ecosystem, supporting various add-on applications such as Libre Office, Prompt Libraries, Agents, and Messaging.

EPrivify is designed to be a cost-effective solution, with a proposed cost of around $35-65 per month or less for its private LLM service running in the cloud. This pricing aims to make private AI technology accessible to a wider audience without hidden costs associated with data exploitation.

We will research the latest open source models. From text-only Llama 3 (8 B and 70 B) and Mixtral 8×22B, multimodal Phi-3 Vision 4.2 B and LLaVA 1.6 (13 B with CLIP)—spanning 4 B to 141 B parameters, context windows up to 128k tokens, and running entirely offline on hardware with roughly 6-48 GB of VRAM.

Models will be updated as needed. Critical patches will be delivered quickly along with monthly or quarterly performance rollups.

Yes. Building task-specific agents is on our roadmap. We’ll start by exposing a lightweight agent framework on top of the core private-LLM stack, so you can create repeatable workflows such as web surfing, data extraction, summarization, or ticket triage.