Self-hosted inference and RAG on your own GPUs, so prompts, embeddings, and responses never leave your network.
Private AI means deploying large language models on hardware you control — on-premises servers, a private cloud, or an air-gapped environment — so that every prompt, response, and embedding stays inside your network. Unlike hosted AI APIs, no data leaves your perimeter and no third party processes, logs, or retains your requests.
Every API call to a hosted model sends your data to a third party's infrastructure. For organizations with sensitive data, that's an unacceptable risk regardless of contractual protections.
Private inference, RAG, and guardrails running entirely on your own hardware — the complete platform for organizations that cannot afford data egress.
Add secure multi-party collaboration to your deployment when you need cross-organizational AI workloads.
Explore Prism AIPrivate AI is artificial intelligence deployed on infrastructure you own and control, so that data processed by the model — prompts, documents, embeddings, and responses — never leaves your network. It is the opposite of hosted AI APIs, which send your data to a third party's servers for processing.
Zero data egress means no part of an AI interaction — the input, the model's intermediate computations, or the response — is transmitted outside your network boundary. There are no API calls to external services, no logging by a vendor, and no data retained by a third party.
Private AI deployment on-premises requires GPU-capable servers (NVIDIA A100, H100, or equivalent), a model-serving runtime such as vLLM or Ollama, and a platform to manage inference, RAG pipelines, and access control. Cube AI packages these components into a single deployable platform with an OpenAI-compatible API.
Private AI means the model runs on your own hardware, so the data never leaves your network. Confidential AI goes further: the model runs inside a Trusted Execution Environment (TEE) where even the infrastructure operator — including your own privileged admins — cannot read the model weights or inference data. For most organizations, private AI satisfies the requirement; confidential AI is used for the highest-assurance workloads.
Any open-weight model that runs on the vLLM or Ollama runtimes is supported: Llama 3, Mistral, Mixtral, Phi-3, Qwen, DeepSeek, and others. Cube AI is model-agnostic — you choose the model based on your capability and hardware budget requirements.
Hardware requirements depend on model size. A 7B parameter model runs well on a single NVIDIA A10 or RTX 4090. A 70B model typically needs multiple A100 80GB or H100 GPUs. Cube AI supports both consumer-grade and data-centre GPUs and can run quantized models to reduce hardware requirements.
Private AI on your own infrastructure eliminates the cross-border data transfer risk that makes hosted AI APIs problematic under GDPR Article 44. Because no data leaves your perimeter, there are no international transfers to third-country cloud providers and no data processing agreements required with an AI vendor. Data protection by design (Article 25) is satisfied at the infrastructure level.
Talk to the team about self-hosted LLM deployment, hardware requirements, and getting started.
