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Core-AI

AI Infrastructure Deployment

Table of Contents
The infrastructure foundation that runs modern AI models securely and efficiently — on-premise or in your private cloud. Core-AI designs and deploys the GPU servers, data layers, and orchestration systems that make private AI viable at enterprise scale.

What We Build
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Running modern open-weight LLMs (Llama 3, Mistral, Qwen, and others) in production requires more than dropping a model behind an API. It requires the right hardware sizing, an efficient inference layer, a vector data plane, and orchestration to route requests, manage models, and integrate with your existing applications.

Core-AI builds this stack end-to-end for organizations that need full sovereignty over their AI compute — financial services, healthcare, public sector, legal, and any business where sending data to third-party APIs is not an option.

Infrastructure Components
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  • AI Model Servers — High-performance inference servers for local LLMs, sized to your throughput and latency targets. NVIDIA H100/A100/L40S or equivalent.
  • Optimized Inference Layer — vLLM, TGI, or TensorRT-LLM tuned for your model and traffic profile, including quantization where appropriate.
  • Vector Data Layer — Private vector databases (Qdrant, Weaviate, pgvector) sized for your corpus and query volume.
  • Orchestration & Routing — Manage multiple models, fallback chains, rate limiting, and request routing across hybrid deployments.
  • Observability & Monitoring — Logs, traces, GPU utilization, token throughput, model accuracy drift.
  • Security & Compliance — Network isolation, audit logging, role-scoped access, and integration with your existing IAM.

System Architecture
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graph LR
  Users((Users)) --> Gate((Gateway))
  Gate --> Serv((Services))
  Serv --> LLMs((Local
LLMs))
  LLMs --> Data((Data
Sys))

  classDef n1 fill:#3b82f6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n2 fill:#6366f1,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n3 fill:#8b5cf6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n4 fill:#a855f7,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n5 fill:#c084fc,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;

  class Users n1;
  class Gate n2;
  class Serv n3;
  class LLMs n4;
  class Data n5;

Deployment Models We Support
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  • Fully on-premise — Your data center, your GPUs, your network. We deploy and you operate.
  • Private cloud (VPC) — Dedicated infrastructure in AWS, Azure, GCP, or OVH — fully isolated from the public LLM APIs.
  • Hybrid — Sensitive workloads run on-prem; non-sensitive or burst capacity routes to commercial APIs through a policy-controlled gateway.
  • Air-gapped — For the most security-sensitive environments where the AI cluster has no external network access at all.

Why Run Private AI Infrastructure
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  • Data sovereignty — No queries, embeddings, or documents ever leave your network.
  • Predictable cost — Fixed infrastructure cost replaces per-token API billing at scale.
  • No vendor lock-in — Open-weight models can be swapped, fine-tuned, or replaced without rewriting your stack.
  • Compliance — Meet GDPR, HIPAA, PCI-DSS, and sector-specific regulations that prohibit third-party data sharing.

Related Services #

Frequently Asked Questions
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What GPU hardware do you recommend for running LLMs on-premise?
Recommendation depends on the model size and throughput requirements. For most enterprise workloads (7B–70B parameter models), we recommend NVIDIA L40S or A100 GPUs. For the largest models or highest throughput, H100s are ideal. We conduct a hardware sizing exercise during the Design phase and provide a bill of materials so you can procure or validate your existing inventory.
Can you deploy on our existing servers, or do we need new hardware?
We assess your existing hardware first. Many organizations have underutilized GPU-capable servers that can run smaller models efficiently. Where new hardware is required, we size it precisely — we do not over-engineer. We also support private cloud deployments (AWS, Azure, GCP, OVH) if on-premise procurement is not viable.
How does on-premise AI infrastructure cost compare to OpenAI API billing?
At moderate-to-high usage, on-premise infrastructure becomes significantly cheaper than per-token API billing — often 60–80% less over a three-year horizon once hardware is amortized. The crossover point typically occurs at a few million tokens per day. We provide a cost model during Discovery so you can build a business case before committing.
Do you support air-gapped environments with no external internet access?
Yes. Air-gapped deployments are a speciality. All models, vector databases, embedding models, and orchestration components are packaged for offline installation. We have experience deploying in classified, regulated, and high-security environments where no external network connectivity is permitted.
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