Building an on-premise AI platform for a Turkish or European Union insurer requires a partner fluent in both the technical and the regulatory sides of that specific combination, which is a narrower set of capabilities than general AI infrastructure experience alone. On the regulatory side, the partner needs to understand the EU AI Act's high risk classification for insurance underwriting and its 2 August 2026 compliance deadline for most high risk obligations, GDPR requirements if EU policyholder data is involved, and Turkey's KVKK data protection law if the insurer operates there, since these regimes have overlapping but not identical requirements around data residency, documentation, and human oversight. On the technical side, the partner needs real GPU infrastructure experience, sizing and installing NVIDIA hardware such as H100 or H200 clusters with Kubernetes based orchestration, and language capability that genuinely covers Turkish alongside English rather than treating Turkish as an afterthought, particularly for document extraction and policyholder facing chatbots. Insurers in this position should ask a potential partner directly about prior work spanning both compliance regimes rather than assuming general AI experience transfers cleanly. Nanobase AI combines on-premise GPU infrastructure deployment with EU AI Act and KVKK aware design and native Turkish and English language capability.
Sizing the GPU infrastructure by workload, not by insurer size
An on-premise AI platform for an insurer is not one infrastructure decision but several, since document extraction, a policyholder chatbot, and fraud model training each carry different GPU memory and throughput requirements.
| Workload | Typical model size range | Practical GPU fit |
|---|---|---|
| Document extraction / OCR pipeline | Small to mid-size vision-language models | Single H100 (80 GB HBM3, 3.35 TB/s) handles substantial throughput with room for concurrency |
| Policyholder chatbot / claims assistant | Mid-size LLM, often served in FP8 | H100 or RTX PRO 6000 (96 GB GDDR7) depending on concurrency needs |
| Fraud model training on claims data | Gradient-boosted trees plus embedding models | Lower GPU demand than LLM serving; a single H100 or A100 (80 GB, 2 TB/s) is often sufficient |
| Large multilingual LLM for Turkish and English | Larger open-weight models needing headroom for KV cache | H200 (141 GB HBM3e, 4.8 TB/s) for single-GPU headroom, or B200 (~180 GB HBM3e, 8 TB/s) for the largest deployments |
Sizing infrastructure around the largest concurrent workload, rather than the insurer's overall size, avoids both an underpowered platform and unnecessary capital spent on capacity no workload actually uses.
Mapping the regulatory requirements side by side
| Requirement | EU AI Act | GDPR | KVKK (Turkey) |
|---|---|---|---|
| Applies to | High-risk AI systems, including insurance underwriting | Personal data processing generally | Personal data processing in Turkey |
| Key deadline | Most high-risk duties from 2 August 2026 (in force since 1 August 2024, GPAI duties from 2 August 2025) | Ongoing since 2018 | Ongoing since 2016 |
| Core documentation | Technical file, risk management, human oversight design | Data protection impact assessment for high-risk processing | Explicit consent basis, data processing inventory |
| Cross-border data transfer | Follows GDPR mechanisms where applicable | Adequacy decisions or standard contractual clauses | Its own transfer approval mechanism, separate from GDPR's |
These three regimes overlap but are not interchangeable, so a compliance program built only against one of them will leave real gaps for an insurer with both EU and Turkish policyholder data.
A deployment roadmap that sequences infrastructure and compliance together
- Run a joint technical and regulatory assessment upfront, identifying which workloads touch EU personal data, Turkish personal data, or both, since this determines which compliance obligations apply to which system component.
- Size and procure GPU infrastructure based on the workload table above, provisioning Kubernetes-based orchestration (such as the NVIDIA GPU Operator) for manageable scaling across workloads.
- Build the compliance documentation, including the EU AI Act technical file and any required data protection impact assessment, alongside infrastructure setup rather than after deployment.
- Pilot one workload, such as document extraction, entirely within the on-premise environment before expanding to additional workloads, validating that data genuinely never leaves the controlled environment.
- Expand to additional workloads and languages once the pilot's compliance documentation and technical performance are both validated.
Sequencing infrastructure procurement and compliance documentation together, rather than treating compliance as a step that follows deployment, is what keeps an on-prem rollout from stalling once EU AI Act obligations come due.
Why Turkish language capability is a technical requirement, not an afterthought
For an insurer with Turkish policyholders, treating Turkish as a secondary language layered onto an English-first system tends to produce measurably weaker performance on document extraction and conversational tasks. This needs to be validated the same way infrastructure sizing is validated, with real Turkish-language test data rather than assumed multilingual capability, a topic covered in more depth in handling claims and documents in Turkish, English, and other languages.
Turkish language validation belongs in the same technical acceptance testing as GPU sizing and throughput, not treated as a separate localization task addressed after the platform is already live.
Frequently asked questions
Does an insurer need separate infrastructure for GDPR versus KVKK compliance?
Not necessarily separate physical infrastructure, but the data governance and access control policies need to account for both regimes' distinct requirements, particularly around cross-border data transfer, which do not automatically align even when the underlying infrastructure is shared.
Is a single H100 enough for a full insurance AI platform?
It depends entirely on the workload mix and concurrency needs. A single H100 can be sufficient for document extraction or a moderate-traffic chatbot, but higher concurrency, longer context windows, or larger models typically require an H200, multiple GPUs, or B200-class hardware.
How long does EU AI Act compliance documentation take to build for an existing on-prem platform?
It depends on how much documentation already exists from the original system design. Building it alongside a new deployment from the start is considerably faster than reconstructing a technical file and risk management documentation retroactively for a system already in production.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, combines on-premise GPU infrastructure sizing and deployment, including Kubernetes GPU Operator setup, with EU AI Act, GDPR, and KVKK-aware design and native Turkish and English language capability, built together from the first assessment rather than as separate workstreams.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.