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Private GPU

AI compute that stays in your jurisdiction.

AI compute that stays in your jurisdiction.

GPU compute is the new electricity — every serious AI workload depends on it. But running AI on AWS, Azure, or Google Cloud means your prompts, your documents, and your data touch US-owned infrastructure subject to US law. For many workloads, that is not acceptable.

What PILOT provides

Dedicated GPU Infrastructure

High-performance GPU servers in EU datacenters, dedicated to your workload. Not shared compute — your GPU allocation, your inference endpoint, your data.

Sized to your requirements — from single-GPU inference deployments for team AI assistants to multi-GPU clusters for model training.

Private LLM Inference

Deploy and run open-weight models — Llama, Mistral, Qwen, DeepSeek, Codestral — on your infrastructure. OpenAI-compatible API endpoint means your existing applications connect without code changes.

Your entire organization uses AI at a fixed monthly cost. No per-token billing. No usage caps. No data leaving your environment.

AI Assistants

Private ChatGPT-like interface for your team — Open WebUI or AnythingLLM, connected to your models and your document library. Ask questions, summarize documents, draft content. All within your infrastructure.

Model Training & Fine-tuning

Fine-tune open-weight models on your proprietary data via LoRA/QLoRA. Your training data, your resulting model, your infrastructure. Models that understand your domain, your terminology, your data.

RAG Deployments

Connect your document libraries to AI via a private vector database. Contracts, manuals, case files, product documentation — AI that answers based on your actual knowledge base, with citations.

AI Model Gateway

One endpoint for all your AI — private and external. LiteLLM running on your infrastructure routes requests across your private models and any external APIs you use (OpenAI, Anthropic, Mistral), with centralized key management, cost tracking, rate limiting per team, and a full audit log of every request.

Your developers use one API endpoint. You decide which model handles which request, what it costs, and who can use how much. Keys stay with you — not distributed across every application.

Autonomous Agents

AI that acts, not just answers. Document processing pipelines, data extraction workflows, automated reporting. Agents that connect to your internal tools via API and run within your infrastructure.

Who this is for

→ Organizations with sensitive data that need AI assistance without sending that data to OpenAI, Anthropic, Google, or similar

→ Legal, healthcare, and financial teams where client data cannot leave controlled infrastructure

→ R&D teams protecting IP — source code, research, proprietary datasets that cannot train commercial models

→ Any organization where legal or compliance has said "no" to commercial AI tools

Pricing model

GPU infrastructure is priced on dedicated allocation — a fixed monthly cost based on the GPU hardware, memory, and storage your deployment requires. You know your cost before you deploy.

No per-token billing. No usage tiers. No surprises when your team uses it heavily.

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  • Hardware — NVIDIA A100 (40/80GB), H100 (80GB SXM), and L40S (48GB) depending on availability and workload requirements. Multi-GPU configurations for training workloads.
  • Inference — vLLM for high-throughput production serving (continuous batching, PagedAttention). Ollama for simpler single-user or low-concurrency deployments.
  • Model sizing — 7B @ Q4 = ~4GB VRAM. 13B @ Q4 = ~8GB. 70B @ INT4 = ~42GB. 70B @ FP16 = ~140GB. We assess and recommend based on quality/throughput requirements.
  • Interface — Open WebUI or AnythingLLM. OpenAI-compatible /v1/chat/completions endpoint. Connects to Cursor, Continue.dev, and any tool with OpenAI API support.
  • RAG — Qdrant (performance-optimized) or ChromaDB (simpler deployments) for vector storage. Embedding models run locally — no external API calls for document indexing.
  • Fine-tuning — LoRA/QLoRA via LLaMA Factory or Axolotl. Training data never leaves your infrastructure. Resulting LoRA adapter merged into base model and served via vLLM.
  • Monitoring — TOWER tracks GPU utilization, VRAM usage, inference latency, and endpoint availability.
  • AI Gateway — LiteLLM proxy in front of vLLM/Ollama and external APIs. Virtual keys per team/app. Cost tracking per model. Rate limiting. Fallback routing (private model → external API if private is overloaded).

Want to build this yourself?

Read the Pilot Book: Private AI Infrastructure — GPU setup, model deployment, vLLM configuration, and RAG implementation.

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