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Privé-AI en on-premise LLM-implementatie

Uw data. Uw modellen. Uw infrastructuur.

Cloud-AI-API's sturen elke prompt en elk antwoord via servers van derden. Voor organisaties met gevoelige intellectuele eigendom, gereguleerde data of kritieke workloads is die ruil onaanvaardbaar. Privé-AI heft hem op: modellen draaien op infrastructuur die u bezit en beheert.

Why Private AI Matters

Organisations across regulated industries are moving AI workloads on-premise for four decisive reasons.

Data Sovereignty & Privacy

Prompts, embeddings, and completions never leave your network perimeter. You retain full ownership and auditability of every byte processed, eliminating third-party data-sharing agreements and residual-use clauses.

Regulatory Compliance

Meet HIPAA, GDPR, SOC 2, ITAR, and sector-specific mandates without relying on a vendor's compliance posture. On-premise deployment gives auditors a clear, controllable surface area.

Low Latency & Deterministic Performance

Eliminate variable round-trip times to cloud endpoints. On-premise inference delivers consistent sub-100ms token latency, enabling real-time applications such as code completion, live chat, and process automation.

Predictable & Declining Cost

Cloud API costs scale linearly with token volume. On-premise hardware is a capital expense that depreciates while throughput increases through model optimisation. At sustained volume, self-hosted inference costs 60-80% less per token.

Cloud API vs On-Premise Deployment

Understanding the trade-offs helps you choose the right approach for each workload.

DimensieCloud-APIOn-premise
Data ResidencyData transits to provider data centres; subject to provider termsData never leaves your network; full custody and audit trail
ComplianceDependent on vendor certifications and shared-responsibility modelsYou own the entire compliance surface; auditors inspect your controls
LatencyVariable; 100-500ms per request depending on load and regionConsistent sub-100ms; no network round-trip to external endpoints
Cost at ScaleLinear per-token pricing; unpredictable spikes during high usageFixed hardware cost amortised over time; 60-80% cheaper at volume
Model CustomisationLimited fine-tuning options; constrained by provider's supported modelsFull control over fine-tuning, quantisation, and model selection
AvailabilitySubject to provider outages, rate limits, and deprecation schedulesSelf-managed uptime with your own redundancy and failover
Setup ComplexityMinimal; API key and SDK integrationRequires hardware provisioning, model deployment, and operations

Models You Can Run Locally

The open-weight ecosystem now offers production-grade models across every capability tier.

Llama 3.1 (8B / 70B / 405B)

Aanbieder: Meta

Best general-purpose open model family. Strong reasoning, instruction following, and multilingual support. The 8B variant runs on a single consumer GPU; the 405B variant rivals frontier closed models.

Het meest geschikt voor: General enterprise assistant, document analysis, code generation

Mistral 7B / Mistral Large

Aanbieder: Mistral AI

Exceptionally efficient inference at the 7B scale. Sliding-window attention enables long-context processing with modest memory. Mistral Large competes with GPT-4 class models.

Het meest geschikt voor: High-throughput classification, summarisation, customer support

Mixtral 8x7B / 8x22B

Aanbieder: Mistral AI

Mixture-of-experts architecture activates only 2 of 8 expert networks per token, delivering 70B-level quality at 12B-level inference cost. Excellent reasoning and code capabilities.

Het meest geschikt voor: Complex reasoning, multi-step analysis, code review

Fine-Tuned & Domain Models

Aanbieder: Your Organisation

LoRA and QLoRA adapters let you specialise any base model on your proprietary data in hours, not weeks. Domain-tuned models outperform general models on narrow tasks by 20-40%.

Het meest geschikt voor: Medical coding, legal contract review, internal knowledge Q&A

Hardware Requirements

Right-sizing your infrastructure depends on model size, concurrency, and latency targets.

Entry

Single NVIDIA RTX 4090 (24 GB VRAM) or Apple M-series Mac with 64 GB unified memory. Runs quantised 7-8B models at interactive speeds for small teams of 5-15 users.

Ondersteunde modellen: Llama 3.1 8B, Mistral 7B, Phi-3

Mid-Range

Dual NVIDIA A100 (80 GB each) or equivalent. Handles 70B-class models with 4-bit quantisation and supports 50-100 concurrent users with batched inference.

Ondersteunde modellen: Llama 3.1 70B, Mixtral 8x7B, CodeLlama 70B

Enterprise

Multi-node clusters with 8x NVIDIA H100 GPUs per node. Required for full-precision 405B models and high-throughput production workloads serving thousands of users.

Ondersteunde modellen: Llama 3.1 405B, Mixtral 8x22B, any model at scale

Industries That Need Private AI

Certain sectors face regulatory, contractual, or operational constraints that make cloud AI impractical or prohibited.

Gezondheidszorg en life sciences

HIPAA requires strict controls over Protected Health Information. On-premise AI enables clinical decision support, medical coding automation, and patient communication without exposing PHI to external processors.

Financiële dienstverlening

SEC, FINRA, and PCI-DSS regulations demand auditable data handling. Private AI powers fraud detection, risk modelling, and client communication while keeping transaction data within the institution's perimeter.

Legal & Professional Services

Attorney-client privilege and confidentiality obligations prohibit sending case materials to third-party APIs. On-premise models enable contract analysis, legal research, and document review under full ethical compliance.

Government & Defence

ITAR, FedRAMP, and classification requirements restrict data movement. Air-gapped deployments ensure AI capabilities are available in secure enclaves without any external network dependency.

Manufacturing & IP-Heavy Industries

Trade secrets, proprietary designs, and process data represent core competitive assets. Private AI enables predictive maintenance, quality analysis, and engineering assistance without IP exposure risk.

Deployment Architecture

A production-grade private AI stack consists of layered components designed for reliability, observability, and scale.

Infrastructure Layer

GPU servers, high-bandwidth networking (NVLink/InfiniBand), shared storage (NFS/Ceph), Kubernetes or Docker orchestration

Model Serving Layer

vLLM or TGI for optimised inference, model registry for version management, quantisation pipeline (GPTQ/AWQ/GGUF), automatic batching and scheduling

API Gateway Layer

OpenAI-compatible REST API, authentication and rate limiting, request routing and load balancing, usage metering and chargeback

Application Layer

RAG pipelines with vector databases, agent frameworks with tool execution, prompt management and A/B testing, monitoring dashboards and alerting

Ontwerp uw private AI-infrastructuur

Ons team helpt u modellen te kiezen, apparatuur te dimensioneren en productieklare private AI-systemen uit te rollen, afgestemd op uw nalevingsvereisten en prestatiedoelen.

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