→ private AI infrastructure services
Private and on-premise AI infrastructure for regulated industries
Self-hosted LLM deployments for clients with regulatory, security, or data-control requirements. EU data residency. VPC-isolated. On-prem on your own GPU servers.
Overview
How we approach this work.
Private AI infrastructure services from Resser cover the full path from procurement of GPU servers to a production-grade LLM stack running entirely inside your environment.
We deploy open-weights models (Llama, Mistral, Qwen, DeepSeek) on vLLM or TensorRT-LLM, behind your auth, inside your VPC or on your own bare metal. We bring the production patterns the closed-source providers have already proven: eval coverage, retry logic, observability, cost telemetry, prompt versioning.
Use cases: regulated industries (healthcare, finance, defense, government), EU data residency under GDPR, IP-sensitive workloads where no prompt or response can leave the perimeter, customers who have already done the procurement and need engineers who can operate the cluster.
What we build
Concrete deliverables.
On-prem LLM stacks
Llama 3 70B, Mistral, Qwen 2.5, DeepSeek on vLLM or TensorRT-LLM. GPU clusters on your bare metal or your private cloud.
VPC-isolated AI deployments
AWS Bedrock in your VPC, Azure OpenAI in your tenant, GCP Vertex in your project. Network egress fully controlled.
EU data residency setups
Frankfurt, Dublin, Paris, Zurich, Stockholm. GDPR-compliant from day one. DPA, SCC, sub-processor list, audit log ready.
Air-gapped AI for defense and government
Fully offline deployments. Model weights, eval data, observability, admin tooling , all inside the perimeter. No outbound network calls.
Private RAG over sensitive data
Document stores, knowledge bases, customer data indexed and served entirely from your infrastructure. No data ever sent to a cloud LLM provider.
GPU operations and capacity planning
H100, H200, A100, L40S, AMD MI300X. Quantization, batching, KV cache, speculative decoding. We size the cluster to your throughput target.
Stack
What we build with.
Inference servers
vLLM, TensorRT-LLM, SGLang, llama.cpp, MLC-LLM. Triton Inference Server for multi-model. Ray Serve for multi-tenant.
Open-weights models
Llama 3 (8B, 70B, 405B), Mistral, Mixtral, Qwen 2.5, DeepSeek, Phi, Gemma. Quantized GGUF, AWQ, GPTQ when memory is the constraint.
GPU hardware
NVIDIA H100, H200, A100, L40S. AMD MI300X. On-prem clusters with NVLink. AWS p5 / p4, Lambda Labs, CoreWeave.
Eval, observability, security
Self-hosted LangFuse for traces. Self-hosted Promptfoo for eval. Prometheus + Grafana. HashiCorp Vault for secrets. RBAC + audit log in Postgres.
Outcomes
What we ship.
Fintech compliance: Llama 3 70B on customer-owned GPUs with no cloud egress; reviewer time-per-case sharply reduced.
Healthcare provider EU deployment: full on-prem RAG over patient documents, GDPR-compliant, sub-second retrieval.
Government deployment: air-gapped LLM with internal eval suite, no outbound network calls.
References with names available after a scoping call.
Related services
Other places this work shows up.
FAQ
Frequently asked.
Why deploy AI on-prem instead of using OpenAI or Anthropic?
Three reasons: data residency / regulatory requirements, IP sensitivity, and predictable unit cost. If your data cannot leave your perimeter or your inference volume is high enough that token pricing exceeds amortized GPU cost, on-prem is the right call. We help you decide in discovery week.
Which open-weights model should we use?
Depends on the task and the hardware. Llama 3 70B for general reasoning. Qwen 2.5 for code and structured outputs. Mistral / Mixtral for cost-efficiency. DeepSeek for math-heavy. We benchmark on your data before locking the choice.
What GPUs do we need?
For a single Llama 3 70B inference instance, an 8× H100 or 8× A100 80GB cluster works well. Quantized variants run on smaller hardware. We do the capacity planning in discovery week and recommend hardware to match your throughput target.
Can you deploy entirely air-gapped?
Yes. We have shipped fully air-gapped deployments for defense and government. Model weights, eval data, observability, admin tooling all inside the perimeter. No outbound network calls. License terms reviewed for each model.
How much does a private AI infrastructure build cost?
Software build (LLM stack, RAG, eval, observability): €40,000-€150,000. Hardware (GPUs) typically procured by the customer; we help with sizing. Annual operations and updates: priced as a monthly retainer.
Want to scope this for your project?
Fill the project-estimate form. We reply within one business day with a preliminary scope and a rough budget bracket.