We design, configure, and deploy the technical foundation your AI strategy needs. GPU servers, network segmentation, security hardening, monitoring — all built for your specific security requirements. Air-gapped, on-premise, private cloud. Zero vendor lock-in.
AI infrastructure consulting is the discipline of building the complete technical environment where your AI systems live. It is not a single product or a vendor platform. It is the full stack — from bare metal GPU servers to inference APIs, from network segmentation to audit logging — designed, configured, and deployed so your AI operates securely, efficiently, and exactly within your constraints.
At BPI, we don't sell hardware. We don't host your AI. We design the infrastructure, configure every component, deploy it on your servers, train your team to operate it, and hand you complete ownership. Everything we build runs on your infrastructure. Everything you own. This is the Zero Data Touch principle applied to infrastructure itself.
The gap between a rack of GPU servers and a production-ready AI system is enormous. It involves driver installation, CUDA optimization, container orchestration, network configuration, security hardening, access control setup, monitoring integration, and countless other steps that require deep expertise. Most organizations try to bridge this gap with cloud AI APIs because the on-premise path seems overwhelming. We make the on-premise path the obvious choice.
We handle the complexity so your team doesn't have to become an AI infrastructure team overnight. We embed with your IT staff, build the system together, document every configuration, and train your people until they can operate and maintain everything independently. When we leave, the infrastructure is yours — fully documented, fully operational, fully under your control.
A law firm's AI infrastructure has different security requirements than a hospital's, which has different requirements than a defense contractor's. We don't use templates. We design every infrastructure build around your specific regulatory environment, your network constraints, and your operational requirements. The result is infrastructure that is purpose-built for your environment, not a one-size-fits-all solution adapted through compromise.
Every infrastructure build starts with understanding your constraints. Book a free consultation and we'll discuss your environment, your requirements, and the infrastructure architecture that fits.
Book a Free ConsultationRead more about our broader Privacy-First AI service, which relies on the infrastructure we build to deliver on-premise AI capabilities.
GPU servers are the computational heart of any on-premise AI system. We select, configure, and optimize the right GPU hardware for your specific model sizes and use cases. This includes NVIDIA RTX 4090 for development and smaller models (7B-13B parameters), NVIDIA A100 for production workloads with 40GB-80GB HBM2e memory, NVIDIA H100 for large-scale inference and fine-tuning with 80GB HBM3 memory, and NVIDIA L40S for virtualized multi-tenant deployments. We handle driver installation, CUDA toolkit configuration, cuDNN and TensorRT optimization, and kernel tuning to ensure maximum throughput and minimum latency for your specific workloads.
Your AI infrastructure must be isolated from your general network to prevent data exfiltration and limit attack surfaces. We design network segmentation architectures that create dedicated AI zones with controlled access paths. For government, defense, and critical infrastructure clients, we deploy fully air-gapped environments — physically isolated networks with no external connectivity, where AI systems operate completely independently. This is the only way to deploy AI in environments where data cannot cross network boundaries under any circumstances.
Every layer of your AI infrastructure is hardened against unauthorized access. We implement zero-trust network design principles, role-based access control (RBAC) integrated with your existing Active Directory or LDAP infrastructure, audit logging for all AI system interactions, encryption at rest using AES-256 and encryption in transit using TLS 1.3, and supply chain security including model provenance verification, software bill of materials (SBOM) generation, and vulnerability scanning of all deployed components.
We set up the complete model hosting stack — containerized model serving with Docker and Kubernetes, inference API endpoints that replace cloud AI integrations, model versioning and rollback capabilities, load balancing across multiple GPU nodes for high availability, and scaling policies that adjust resources based on demand. Your AI models run on your infrastructure with the same performance characteristics as cloud-hosted alternatives, but with complete data containment.
Retrieval-Augmented Generation (RAG) pipelines require vector databases to store and retrieve embeddings from your proprietary documents. We deploy and configure vector databases such as Milvus, Qdrant, or Weaviate, set up embedding models optimized for your domain, configure semantic search pipelines, and implement chunking strategies that preserve document context. This infrastructure enables your AI to answer questions based on your proprietary knowledge base without exposing that data externally.
You cannot manage what you cannot measure. We deploy comprehensive monitoring stacks using industry-standard tools like Prometheus for metrics collection, Grafana for visualization and dashboards, Datadog for integrated observability, structured audit logging for compliance requirements, and alerting rules that notify your team of anomalies, performance degradation, or security events. Your AI infrastructure is monitored 24/7 with the same rigor applied to your production systems.
For organizations with existing data center infrastructure, we design AI deployments that integrate with your current hardware, power, cooling, and network topology. We assess your data center's readiness for GPU workloads (power density, thermal capacity, rack space), configure servers for optimal performance within your physical constraints, and integrate with your existing storage systems, backup infrastructure, and network switches. The result is AI infrastructure that feels like a natural extension of your data center, not a foreign system bolted on.
For organizations already invested in hybrid cloud environments, we deploy AI infrastructure on private cloud platforms that bring cloud services to your on-premise data center. AWS Outposts, Azure Stack HCI, and Google Anthos provide familiar cloud interfaces while keeping all data within your physical facility. We configure GPU instances, set up container orchestration, and integrate your AI services with your existing private cloud management tools — giving you cloud-like flexibility without data leaving your environment.
Air-gapped environments represent the highest security tier for AI deployment. These networks have no physical or wireless connection to external networks — no internet, no VPN, no data links of any kind. Deploying AI in air-gapped environments requires specialized handling: offline model downloads, physical media transfer procedures, manual SBOM verification, and air-gapped monitoring solutions that operate entirely within the isolated network. We have extensive experience deploying AI infrastructure in classified and sensitive environments where data exfiltration is not just a risk — it is a violation of national security.
Not every environment requires complete isolation. Hybrid AI infrastructure segments your AI systems from general network access while allowing controlled connections through API gateways and data diodes. This approach enables AI systems to interact with external services (like translation APIs or search indexes) without exposing your proprietary data. We design hybrid architectures that give your AI the connectivity it needs while maintaining strict data boundaries — every data flow is logged, monitored, and controlled.
AI is not limited to data centers. We deploy compact AI infrastructure at the edge — in field offices, remote facilities, manufacturing floors, and branch locations. Edge deployments use smaller GPU configurations (single GPU workstations or compact server forms) optimized for specific tasks like document processing, image analysis, or real-time inference. These edge systems operate independently or synchronize with central infrastructure through controlled data channels, ensuring AI capabilities are available wherever your organization operates.
Zero-trust architecture assumes that no entity — inside or outside your network — should be trusted by default. Every request to your AI infrastructure is authenticated, authorized, and encrypted. We implement micro-segmentation to isolate AI workloads from each other, mutual TLS authentication between all service components, network policies that deny all traffic by default and permit only explicitly allowed flows, and continuous verification of user and machine identities. Your AI infrastructure operates on the principle that a breach is inevitable — and every layer is designed to contain it.
Not everyone who interacts with your AI systems needs the same level of access. We implement granular RBAC policies that define exactly what each role can do — which models they can query, which documents they can access through RAG pipelines, which administrative functions they can perform. RBAC is integrated with your existing identity infrastructure (Active Directory, LDAP, or SAML-based SSO) so your team's existing credentials work seamlessly with your AI systems. Access reviews are scheduled and automated, ensuring permissions are current and auditable.
Every interaction with your AI infrastructure generates an audit log: who queried which model, when, from what IP address, with what input parameters, and what output was returned. We configure comprehensive audit logging that meets your regulatory requirements — HIPAA audit controls, SOX financial reporting integrity, FedRAMP continuous monitoring, NERC CIP access logging. Logs are tamper-evident, centrally collected, and retained according to your compliance schedule. During an audit, you won't scramble for evidence. Your AI infrastructure already provides it.
Data protection extends to every state your data exists in. At rest, we implement AES-256 encryption for GPU memory, model weights, vector database contents, and stored documents — using hardware-enforced encryption where GPUs provide dedicated secure memory regions. In transit, all data flowing between AI system components uses TLS 1.3 with mutual authentication. For air-gapped deployments, encryption keys are managed offline through hardware security modules (HSMs) or dedicated key management servers that never connect to network interfaces.
Your AI models are software, and software has vulnerabilities. We implement supply chain security practices that verify every component before it reaches your infrastructure. Model provenance tracking ensures you know exactly which model version, trained on which data, from which source. Software bill of materials (SBOM) generation catalogs every library, dependency, and package in your AI stack. Continuous vulnerability scanning identifies known CVEs in your deployed components and alerts you to emerging threats. Your AI infrastructure is as secure as its least-secure component — and we secure every component.
Our AI infrastructure security follows a defense-in-depth model with six layers:
Each layer operates independently. If one layer is compromised, the remaining layers contain the breach and maintain data integrity. This is bullet-proof by design.
Choosing the right GPU is the most critical hardware decision for your AI infrastructure. The table below maps model sizes and use cases to recommended GPU configurations:
| GPU Model | VRAM | Best For | Model Size | Storage | Network | Estimated Cost |
|---|---|---|---|---|---|---|
| NVIDIA RTX 4090 | 24GB GDDR6X | Development, testing, small model inference | 7B-13B parameters (quantized) | 2TB NVMe SSD | 1GbE minimum | $1,600-$2,000 |
| NVIDIA A100 | 40GB-80GB HBM2e | Production inference, fine-tuning, RAG pipelines | 13B-70B parameters | 4TB NVMe SSD RAID | 25GbE recommended | $10,000-$18,000 |
| NVIDIA H100 | 80GB HBM3 | Large-scale inference, multi-model serving, training | 70B+ parameters | 8TB NVMe SSD RAID | 100GbE / InfiniBand | $25,000-$40,000 |
| NVIDIA L40S | 48GB GDDR6 | Virtualized multi-tenant, GPU computing | Multiple models simultaneously | 4TB NVMe SSD RAID | 25GbE minimum | $8,000-$12,000 |
Beyond GPU memory, your system requires sufficient CPU RAM, fast storage, and adequate network bandwidth. For a typical production AI deployment: CPU RAM should be 2x-4x the GPU VRAM to handle data preprocessing and model loading (64GB-256GB DDR4/DDR5), storage should use NVMe SSDs with a minimum of 2TB for model weights, vector databases, and document stores (scale to 8TB+ for large document corpora), and network bandwidth between GPU nodes in multi-GPU setups should use NVIDIA NVLink or InfiniBand for inter-GPU communication at 400Gb+ speeds.
Network architecture directly impacts AI system performance. Internal network latency between your application servers and GPU nodes should be under 1ms using 25GbE or higher. External-facing API endpoints require bandwidth allocation based on expected concurrent users — a rule of thumb is 100Mbps per 10 concurrent inference requests for 7B-parameter models, scaling linearly with model size. For air-gapped environments, network design focuses on isolation correctness rather than throughput, but internal segment bandwidth still matters for data pipeline performance.
The total cost of ownership comparison between on-premise and cloud AI infrastructure favors on-premise for organizations with sustained AI usage. Cloud AI pricing (per-token or per-hour GPU rental) scales linearly with usage and becomes exponentially expensive at production scale. On-premise infrastructure requires upfront capital expenditure but has predictable ongoing costs (electricity, maintenance, staff time). For organizations processing more than 100,000 requests per month, on-premise AI typically achieves 60-80% lower total cost of ownership within the first year.
| Cost Factor | On-Premise AI Infrastructure | Cloud AI Infrastructure |
|---|---|---|
| Upfront Hardware | $15,000-$80,000 (GPU servers, networking, storage) | $0 |
| Monthly GPU Cost (40 hrs/mo) | $0 (amortized hardware cost ~$500-$2,000/mo) | $500-$4,000 (A100/H100 spot instances) |
| Per-Token Inference | $0.000001-$0.00001 (electricity + maintenance) | $0.00001-$0.0001 (vendor markup) |
| Data Transfer | $0 (internal processing) | $0.01-$0.10 per GB egress |
| 12-Month TCO (100K requests/mo) | $25,000-$50,000 | $60,000-$120,000 |
| Data Security | Complete data containment | Vendor-dependent, data leaves your network |
Read our How It Works page to understand an engagement process from assessment through deployment.
Your AI infrastructure needs to integrate seamlessly with your existing applications. We design API gateway architectures that expose your AI capabilities through standardized REST and GraphQL endpoints, implement rate limiting and request queuing to manage GPU resource allocation, configure authentication tokens and API key management for application-level access control, and provide SDKs and integration examples for common development frameworks. Your existing applications connect to your on-premise AI the same way they connect to any other service — with the critical difference that all AI processing happens within your infrastructure.
We integrate your AI infrastructure's access controls with your existing identity management systems. Active Directory integration provides seamless single sign-on for Windows-domain environments, LDAP integration supports directory services across Linux and multi-platform environments, SAML-based SSO connects to identity providers like Okta, Azure AD, and OneLogin, and RBAC policies map your organizational roles to AI system permissions. Your team uses their existing credentials. Your IT team manages access through tools they already know.
Your AI systems are only as valuable as the data they can access. We build data pipeline integrations that connect your AI infrastructure to your existing systems — EHR systems (Epic, Cerner, Allscripts) for clinical document processing, CRM platforms (Salesforce, HubSpot) for customer intelligence, document management systems (NetDocs, iManage, SharePoint) for legal and enterprise knowledge retrieval, core banking systems (Fiserv, Jack Henry) for financial data analysis, and custom databases through SQL and NoSQL connectors. Data flows from your systems to your AI for processing, and results flow back — without data ever leaving your network.
Your AI infrastructure integrates with your existing monitoring stack. Prometheus exporters collect GPU utilization, memory usage, inference latency, and throughput metrics. Grafana dashboards provide real-time visualization of system health, model performance, and resource utilization. Datadog integration provides centralized observability across your entire IT infrastructure, including AI workloads. Alerting rules connect to your existing notification channels (PagerDuty, Slack, email) so your team is notified of issues immediately. Your AI infrastructure is visible through the same tools you already use.
Not sure which GPU configuration your organization needs? Book a free consultation and we'll help you right-size your infrastructure — no over-provisioning, no under-performance.
Book a Free ConsultationEvery infrastructure build comes with comprehensive documentation that your team can reference independently. Architecture diagrams show every component, connection, and data flow in your AI infrastructure. Runbooks provide step-by-step procedures for deployment, scaling, backup, and recovery. Troubleshooting guides cover common issues, diagnostic commands, and resolution steps. Configuration manifests are version-controlled and documented so your team understands why every setting exists, not just what it is. When we leave, you're not dependent on us to understand your system.
Documentation is necessary but insufficient. We conduct hands-on training sessions for your operations team (infrastructure management, monitoring, scaling), your security team (access control configuration, audit log review, vulnerability management), and your development team (API integration, model fine-tuning, RAG pipeline optimization). Training is conducted on your actual infrastructure, using your actual data, so the knowledge transfers directly to your operational environment. We don't train on generic examples — we train on your system.
When issues arise — and they will — your team needs to know exactly what to do. We create incident response playbooks specific to your AI infrastructure: GPU failure procedures (failover, replacement, data recovery), network segmentation breach response (containment, investigation, restoration), model compromise detection (identification, rollback, retraining), and data pipeline failure recovery (source system diagnostics, re-sync procedures, data integrity verification). These playbooks are tested during training so your team is prepared before an incident occurs.
While complete hand-off is our standard, some organizations prefer an ongoing advisory relationship. Our optional retainer program provides quarterly infrastructure reviews, model update recommendations as new versions are released, security advisory updates as new vulnerabilities emerge, and ad-hoc consulting when your team needs guidance. There are no lock-in contracts. No data access. Just expertise available when you need it.
Learn more about our complete engagement process on our How It Works page, including our discovery, assessment, architecture, build, training, and hand-off methodology.
The right GPU depends on your model sizes, expected workload, and budget. For development and small models (7B-13B parameters), an NVIDIA RTX 4090 with 24GB VRAM is sufficient and cost-effective. For production deployments handling 13B-70B parameter models, we recommend NVIDIA A100 with 40GB-80GB HBM2e memory. For large-scale inference and multi-model serving, NVIDIA H100 with 80GB HBM3 provides the highest throughput. For virtualized multi-tenant environments running multiple models simultaneously, NVIDIA L40S offers the best balance of capacity and cost. We assess your specific requirements during the discovery phase and provide a detailed hardware recommendation tailored to your use case. Book a consultation and we'll help you right-size your infrastructure.
Absolutely. We design AI infrastructure with scalability as a first principle. You can start with a single GPU workstation for development and proof-of-concept, expand to a multi-GPU server for production workloads, and scale to a full GPU cluster for enterprise-wide deployment. Our architecture uses containerized components (Docker, Kubernetes) that scale horizontally — adding another GPU node is a matter of provisioning hardware and integrating it into the existing cluster. We recommend starting with a clear architecture that supports your 2-3 year roadmap, even if you only deploy the first phase immediately. This prevents costly re-architecture when you scale.
Air-gapped environments require specialized procedures because no component can connect to external networks. We handle this through offline model acquisition (downloading models and dependencies at an authorized location and transferring them via physical media), air-gapped container registries (storing Docker images on isolated storage that is physically moved into the air-gapped network), manual SBOM verification (documenting every software component's provenance before physical transfer), and air-gapped monitoring (using offline-capable monitoring tools that collect metrics locally and export them through controlled data channels). We have deployed AI infrastructure in classified government environments and critical infrastructure facilities where air-gapping is mandatory. The process is more complex but the security benefits are unmatched.
We deploy comprehensive monitoring using industry-standard tools that integrate with your existing observability stack. Prometheus collects GPU utilization, memory usage, inference latency, throughput, and error rates. Grafana provides real-time dashboards for system health and performance trends. Datadog offers centralized observability across your entire IT infrastructure, including AI workloads. We configure alerting rules that notify your team via PagerDuty, Slack, or email when anomalies are detected — GPU failures, performance degradation, unauthorized access attempts, or data pipeline disruptions. Your AI infrastructure is monitored with the same rigor as your production systems, and your team manages it through tools they already use.
Supply chain security is critical because your AI models are software, and software has vulnerabilities. We implement a three-layer approach: Model Provenance Tracking — every model is traced to its source, including version, training data summary, and any modifications made. We only deploy models from verified sources. Software Bill of Materials (SBOM) — every library, dependency, and package in your AI stack is catalogued and version-tracked, providing complete transparency of what runs in your infrastructure. Continuous Vulnerability Scanning — deployed components are regularly scanned against CVE databases for known vulnerabilities, with automated alerts and remediation procedures. For government and defense clients, we extend this to include DoD-suppliant SBOM formats and manual security reviews of all deployed components.
Book a free 30-minute consultation. We'll discuss your environment, your security requirements, and the infrastructure architecture that fits. No pressure. No pitch deck. Just an honest conversation about building AI infrastructure that keeps your data exactly where it belongs.
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