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Dflare AI

Enterprise GPU Infrastructure Platform​

Transforming bare metal GPU servers into production-ready, multi-tenant compute environments — delivering the raw power of dedicated hardware with the operational simplicity of a managed cloud service.

Version 1.0 | 2026

01 Executive Summary​

The rapid advancement of artificial intelligence has fundamentally changed the requirements for compute infrastructure. Modern workloads — particularly large-scale model training, fine-tuning, and real-time inference — demand high-density GPU clusters, ultra-low-latency interconnects, and sustained high-throughput data access.

Traditional cloud architectures, designed for general-purpose compute, are increasingly misaligned with these requirements. At the same time, building and operating dedicated GPU infrastructure introduces significant complexity across hardware provisioning, cluster orchestration, networking, storage, and security.

KEY INSIGHT

Dflare AI addresses this gap by delivering a fully managed, enterprise-grade GPU infrastructure platform that transforms bare metal GPU servers into production-ready, multi-tenant compute environments.

The platform provides unified support for Kubernetes and Slurm workloads, high-performance storage integrated over InfiniBand fabric, hardware-enforced tenant isolation, and comprehensive lifecycle automation — all accessible through a single control plane.

02 Introduction​

Artificial intelligence is transitioning from experimental workloads to mission-critical infrastructure. Organizations across industries are deploying AI systems that require:

  • Large-scale distributed training environments
  • Reliable and low-latency inference platforms
  • Secure, multi-tenant resource sharing
  • Transparent usage tracking and cost control

These requirements place significant pressure on infrastructure systems, exposing limitations in both public cloud offerings and traditional on-premises deployments. This paper outlines the challenges of modern AI infrastructure and presents Dflare AI as a purpose-built solution.

03 The AI Infrastructure Challenge​

Hardware Complexity​

Modern GPU servers are highly specialized systems. Each node may include multiple high-performance GPUs, high-bandwidth interconnects, and complex NUMA configurations. Achieving optimal performance requires precise tuning at both the hardware and operating system levels.

Fragmented Compute Paradigms​

AI workloads typically span two operational models: Kubernetes for containerized pipelines, inference, and MLOps workflows, and Slurm (HPC) for batch scheduling and large-scale training jobs. Most platforms support one paradigm effectively, but not both in a unified environment.

Multi-Tenancy and Isolation​

Sharing GPU infrastructure across teams or customers introduces challenges in ensuring strict isolation across network communication, storage access, compute resources, and identity management. Software-only isolation is insufficient in high-performance environments.

Data Throughput Requirements​

AI workloads are highly data-intensive. Training processes require sustained access to large datasets, often at throughput levels that exceed traditional storage architectures.

Operational Overhead​

Managing GPU infrastructure involves provisioning hardware, maintaining clusters, monitoring performance, enforcing compliance, and tracking usage for billing. These requirements create a significant operational burden for most organizations.

04 Dflare AI Platform Overview​

Dflare AI is a GPU-native infrastructure platform designed to address the full lifecycle of AI compute operations. It enables organizations to:

  • Provision bare metal GPU resources on demand
  • Deploy Kubernetes clusters powered by CKP (Coredge Kubernetes Platform) and Slurm clusters through a unified interface
  • Access high-performance storage integrated with compute fabric
  • Enforce multi-tenant isolation across all infrastructure layers
  • Monitor system performance and resource utilization
  • Track and report usage with granular billing metrics
  • Run ML workloads through an integrated ML Platform with GPU notebooks, distributed training, LLM inference, fine-tuning, and experiment tracking

The platform abstracts infrastructure complexity while preserving direct access to hardware performance.

Eight Operational Pillars​

Eight Operational Pillars

Exhibit 1: Dflare AI automates eight critical operational pillars.

05 Architecture Overview​

Dflare AI is built on a modular, microservices-based architecture with clearly defined layers spanning control plane, compute, networking, storage, identity, and observability.

Platform Architecture​

Platform Architecture

Exhibit 2: Platform architecture — from control plane to infrastructure.

Control Plane​

Provides orchestration capabilities through a web-based portal, REST APIs, and workflow automation services.

Compute Layer​

Manages bare metal GPU nodes, Kubernetes cluster orchestration, and Slurm-based HPC scheduling on shared physical infrastructure.

Network Fabric​

Dual-fabric architecture: Ethernet frontend for control/management, InfiniBand backend for GPU-to-GPU and GPU-to-storage communication. Isolation via VRF, VLAN, and IB partition keys.

Storage Layer​

High-performance parallel filesystem over InfiniBand for training workloads, and object/file storage for platform services. Dual-layer isolation.

Identity and Access​

Enterprise IAM integration with OAuth2/OIDC, RBAC + ABAC, and per-tenant isolation of identity domains.

Observability and Metering​

Real-time monitoring with GPU telemetry, node metrics, and cluster state. Granular usage aggregation for billing per tenant, project, and user.

Network Architecture​

DUAL-FABRIC NETWORK ARCHITECTURE​

Network Architecture

Exhibit 3: Dual-fabric network architecture with hardware-enforced tenant isolation.

06 Core Capabilities​

Unified Kubernetes and Slurm Orchestration​

Dflare AI supports both containerized and HPC workloads on the same infrastructure. Kubernetes clusters are powered by CKP (Coredge Kubernetes Platform), enabling organizations to run diverse AI workloads without duplicating environments, standardize operations across teams, and optimize resource utilization.

Bare Metal Performance​

The platform provides direct access to GPU hardware without virtualization overhead. System-level optimizations are applied automatically, ensuring consistent and predictable performance. Standard GPU slicing via NVIDIA MIG (Multi-Instance GPU) profiles enables partitioning supported GPUs into isolated instances for efficient resource utilization.

Automated Provisioning Lifecycle​

Automated Provisioning Lifecycle

Exhibit 4: Automated bare metal provisioning lifecycle.

Multi-Tenant Isolation​

Isolation is enforced across multiple layers: network segmentation using VRF and VLAN, InfiniBand partitioning for GPU communication, storage-level access controls, and compute-level resource isolation. This multi-layer approach ensures strong separation between tenants.

High-Performance Storage Integration​

Storage is tightly integrated with the compute fabric, enabling high-throughput data access, parallel I/O operations, and efficient handling of large datasets.

Automated Lifecycle Management​

The platform automates infrastructure provisioning, cluster deployment, scaling and upgrades, and resource deallocation — reducing operational overhead and accelerating time to production.

Monitoring and Billing​

Comprehensive visibility into system performance, real-time resource usage metrics, and detailed billing and reporting capabilities. Every GPU-hour, CPU-hour, and storage byte is tracked.

ML Platform​

Integrated machine learning environment providing GPU notebooks, distributed training, LLM inference with OpenAI-compatible APIs, model fine-tuning, experiment tracking with MLflow, and dataset management — enabling the complete ML lifecycle within workspace isolation.

Tenant Isolation Model​

MULTI-LAYER TENANT ISOLATION​

Tenant Isolation Model

Exhibit 5: Multi-layer tenant isolation — each layer independently enforced.

07 Security and Compliance​

Dflare AI implements a defense-in-depth security model based on zero-trust principles. No action is trusted by default; every request is authenticated and authorized at multiple levels.

  • Authentication via OAuth2/OpenID Connect with short-lived JWT tokens and MFA support
  • Authorization using RBAC and ABAC with per-tenant IAM realms
  • Encrypted communication using TLS 1.2+ and mTLS between internal services
  • Immutable audit logging with correlation IDs for full traceability
  • Hardware-enforced network isolation via VRF, VLAN, and InfiniBand partition keys

Compliance Alignment​

StandardControl AreaImplementation
NIST 800-53 Rev 5Access ControlRBAC via IAM, SSO, scoped tokens, tenant realm isolation
NIST 800-53 Rev 5AuditImmutable logs, session tracking, telemetry pipeline
ISO/IEC 27001Access / CryptoIAM with RBAC, TLS/mTLS, PKI, certificate lifecycle
HIPAAAccess / AuditIAM, ACLs, immutable logs, session playback

Defense-in-Depth Security Model​

Defense-in-Depth Security Model

Exhibit 6: Concentric defense-in-depth security model.

08 Use Cases​

Large-Scale Model Training​

Distributed training across multi-node GPU clusters with high-speed interconnects and RDMA collective operations. Parallel filesystem delivers the storage throughput to keep GPUs fed.

Model Fine-Tuning and RLHF​

Secure, isolated environments for adapting models to proprietary datasets with dedicated GPU allocation, isolated storage, and job-level accounting.

AI Inference at Scale​

Scalable deployment of inference workloads with GPU resource requests, auto-scaling, load balancing, and performance monitoring through integrated dashboards.

High-Performance Computing​

Scientific simulation, computational fluid dynamics, molecular dynamics, and climate modeling on Slurm with GPU-aware batch scheduling.

Enterprise MLOps​

End-to-end pipelines for model development, deployment, and monitoring with Kubernetes-native workflows and persistent storage for datasets and model artifacts.

09 Target Customers​

Dflare AI is designed for organizations requiring high-performance, secure, and scalable GPU infrastructure:

SegmentUse CaseKey Platform Benefit
Cloud Service ProvidersGPU compute for end customersMulti-tenant isolation, billing, scale
AI Research InstitutionsModel training and experimentationSlurm + K8s, fair-share scheduling
Enterprise AI TeamsProduction ML and inferenceK8s-native, monitoring, RBAC
Sovereign AI ProgramsNational AI infrastructureAir-gapped, compliance, data sovereignty
Regulated IndustriesHealthcare, financeHIPAA/NIST alignment, audit trails

10 Key Differentiators​

Unified Kubernetes + HPC. Run both containerized and Slurm workloads on the same bare metal infrastructure with unified networking, storage, security, and billing.

True Bare Metal Performance. Direct GPU access without virtualization overhead. Hardware-level BIOS and OS tuning pre-applied via golden images for consistent, predictable performance.

Hardware-Enforced Multi-Tenant Isolation. Isolation at InfiniBand switch hardware (partition key), filesystem (access control map), and network fabric (VRF/VXLAN). Double isolation on storage ensures defense-in-depth.

Compute and Storage Over High-Speed Fabric. Parallel filesystem over InfiniBand delivers storage throughput that matches GPU compute appetite.

Full Lifecycle Automation. From bare metal power-on to production cluster — fully automated. No SSH, no manual configuration, no ticket-based provisioning.

Vendor-Agnostic GPU Support. Supports NVIDIA, AMD, and Intel accelerators — no vendor lock-in.

KEY INSIGHT

Dflare AI uniquely combines unified K8s and HPC orchestration, bare metal performance, hardware-enforced isolation, and full lifecycle automation in a single platform — a combination not available from any single public cloud provider.

11 Conclusion​

The increasing scale and complexity of AI workloads require a new approach to infrastructure — one that combines performance, flexibility, and operational efficiency.

Dflare AI provides a unified platform that simplifies the deployment and management of GPU infrastructure while maintaining the performance characteristics required for advanced AI workloads.

By integrating compute, storage, networking, and orchestration into a single system, Dflare AI enables organizations to focus on building and deploying AI solutions rather than managing infrastructure.

Dflare AI is not just another cloud platform. It is the operating system for enterprise GPU infrastructure.

Version 1.0 | 2026

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