High-performance workloads like AI training, blockchain validation, and financial analytics create a challenging equation. You need maximum computational power while protecting your most sensitive data. Traditional security models force you to choose between speed and confidentiality, but confidential computing changes this dynamic by securing data while it’s actively being processed without performance compromises.
The convergence of hardware-based security technologies with GPU acceleration creates new possibilities for secure, high-throughput computing. Organizations can now run confidential workloads that were previously impossible or impractical. This includes anything from collaborative AI training between competing institutions to real-time fraud detection across multiple financial networks.
Why High-Performance Workloads Need Confidential Computing
Modern computing workloads handle increasingly sensitive information at unprecedented scales. Financial services, healthcare, and government sectors are subject to strict compliance regimes and benefit from protected, isolated compute environments to process regulated data. When you’re training AI models on proprietary datasets, processing financial transactions in real-time, or running blockchain validators with sensitive metadata, traditional security measures fall short.
The challenge intensifies with high-performance requirements. GPU-accelerated workloads process massive datasets containing trade secrets, personal health information, or financial records. Organizations that need to combine multiple private data sets can use confidential computing to perform joint analysis or offer confidential AI services without exposing anyone’s private data. This capability transforms industries where data collaboration was previously blocked by security or compliance concerns.
Multi-party computation (MPC) scenarios particularly benefit from confidential computing infrastructure. Financial institutions can perform joint analytics on encrypted customer data and transactions, enabling better risk assessment, fraud detection, and money laundering investigation. However, traditional MPC implementations often suffer performance penalties that make them impractical for real-time applications.
Hardware-Based Security Meets Performance Requirements
Intel Trust Domain Extensions (TDX) and Intel Software Guard Extensions (SGX) represent the latest evolution in hardware-based confidential computing. Intel TDX aims to isolate VMs from the host and hypervisor and protect VMs against a broad range of software and hardware attacks. Each VM is hardware-isolated into a Trust Domain (TD), which helps strengthen customers’ control of their data and IP.
The technology provides several critical security features:
Hardware-Isolated Trust Domains: TDX creates isolated execution environments at the hardware level, protecting workloads from the hypervisor, host operating system, and other tenants. This isolation extends to memory encryption, where hardware-based memory encryption helps ensure your data and applications can’t be read or modified while in use.
Remote Attestation: A key feature of Intel TDX is remote attestation, which gives customers the ability to verify their VM is running with memory and CPU state confidentiality and integrity in a hardened environment. This cryptographic verification ensures workloads are running in genuine confidential environments.
Measured Boot and Cryptographic Verification: The hardware provides cryptographic evidence of system integrity from boot through runtime, enabling organizations to verify their confidential computing environment hasn’t been compromised.
GPU Integration for Confidential High-Performance Computing
The integration of GPU acceleration with confidential computing represents a significant breakthrough. Cloud providers are now delivering confidential GPUs that extend security to data-intensive AI and ML workloads by having Intel TDX enabled on the CPU and NVIDIA Confidential Computing enabled on the GPU. This dual-layer protection addresses the complete compute stack.
While Intel TDX secures the CPU and memory operations, NVIDIA’s confidential computing technologies protect GPU operations. The NVIDIA H100 Tensor Core GPUs extend the trusted execution environment from the CPU to the GPU, enabling confidential computing for accelerated workloads. This implementation creates a hardware-based trusted execution environment that secures and isolates workloads running on GPU infrastructure.
For organizations running AI training, financial modeling, or blockchain operations, this means you can process sensitive data at full GPU speeds without security compromises. The secure channel between CPU and GPU maintains confidentiality throughout the entire compute pipeline.
OpenMetal’s Confidential Computing Infrastructure
OpenMetal V4 bare metal servers provide purpose-built infrastructure for confidential computing workloads with both security and performance optimization. The platform supports Intel Trust Domain Extensions (TDX) and Intel Software Guard Extensions (SGX) across multiple server configurations, with minimum requirements including 1 TB of total memory and 8 DIMMs per CPU with proper BIOS configuration.
XL V4 Server Specifications: These systems use dual Intel Xeon Gold 6530 CPUs with 1 TB DDR5-5600 RAM and up to eight 6.4TB Micron 7450 MAX NVMe drives. The high-speed DDR5 memory and NVMe storage are specifically designed for high-throughput workloads that require both security and performance.
XXL V4 Server Specifications: For the most demanding workloads, XXL V4 servers provide 2 TB DDR5-4800 RAM with six 6.4TB Micron 7450 NVMe drives. These systems are engineered for GPU integration and memory-intensive confidential computing applications.
GPU Integration: Dedicated GPU servers and GPU clusters integrate seamlessly with confidential computing support, allowing secure execution of AI and HPC workloads on GPU infrastructure. H100 GPUs can be attached to Intel TDX-enabled VMs using PCIe passthrough maintaining security isolation while delivering full GPU performance.
Network Performance: Dual 10 Gbps uplinks per server on both private and public networks provide 20 Gbps total bandwidth. Customers receive isolated VLANs with unmetered intra-cluster traffic to avoid bottlenecks during GPU or encryption-heavy workloads.
Rapid Deployment: Private clouds can deploy in about 45 seconds, and additional servers can be added to clusters in about 20 minutes. This supports rapid scale-out of confidential workloads without the performance penalties common in public cloud environments.
Use Cases: Where Security Meets Performance Demands
AI and Machine Learning Training: Organizations can now train models on sensitive datasets while maintaining confidentiality. Hospitals can collaborate on cross-institution research without sharing patient records. The combination of confidential computing with GPU acceleration makes collaborative AI training practical at enterprise scale. For detailed guidance on this application, see our post on confidential computing for AI training.
Blockchain Validator Operations: Blockchain validators require high computational throughput while protecting sensitive metadata, validator keys, and transaction data. Confidential computing provides hardware-level protection for these critical operations without impacting validation performance. Learn more about blockchain infrastructure on bare metal.
Financial Analytics and Trading: Real-time financial modeling and algorithmic trading demand both computational speed and regulatory compliance. Banks can jointly screen transactions for anti-money laundering and assess creditworthiness without revealing customer details. Confidential computing enables secure financial analytics that meets regulatory requirements while maintaining trading speed.
Multi-Party Computation at Scale: Traditional MPC implementations often suffer performance bottlenecks. Confidential-computing powered MPC platforms enable joint data analytics where privacy or compliance issues would otherwise block collaboration, including collaborative research, data sharing among competitors, secure financial transactions, and secure data pooling.
Healthcare Data Processing: Healthcare organizations can process protected health information while maintaining HIPAA compliance. Confidential computing provides the hardware-level protection required for sensitive medical data analysis and research collaboration.
Performance Considerations and Best Practices
Confidential computing introduces computational overhead, but modern hardware implementations minimize performance impact. Intel processors provide hardware acceleration for AI workloads even within confidential environments, maintaining performance while securing data in use.
Memory Management: The memory encryption and isolation features of TDX require careful memory allocation planning. Organizations should allocate sufficient memory headroom for the security overhead while maintaining performance targets.
Network Optimization: Confidential workloads benefit from dedicated network paths to minimize latency. OpenMetal’s VLAN segmentation provides isolated network environments that support both security and performance requirements.
Storage Architecture: High-performance confidential computing workloads require storage architectures that can handle both encryption overhead and throughput demands. Ceph storage integration provides scalable, secure storage that matches the performance characteristics of confidential computing infrastructure.
Cost Planning: Understanding the total cost of confidential computing infrastructure is crucial for budget planning. Use our cloud deployment calculator to model different configurations and find the right balance between security, performance, and cost.
Security Validation and Attestation
Remote attestation provides cryptographic proof that confidential computing environments are operating correctly. Attestation provides stakeholders cryptographic evidence that their confidential VM is genuine, up to date within policy, and launched using authenticated firmware. This validation process is critical for high-stakes applications where security assurance is paramount.
Organizations can verify their workload integrity without relying solely on cloud provider assurances. The hardware-based attestation mechanisms built into Intel TDX and other confidential computing technologies provide independent verification of system security state.
Infrastructure Future-Proofing
The evolution of confidential computing technology creates opportunities for organizations to future-proof their infrastructure. As regulatory requirements continue to expand and data sensitivity increases, having confidential computing capabilities built into your infrastructure provides flexibility for future security requirements.
The convergence of hardware-based security, GPU acceleration, and bare metal control creates new possibilities for secure computing at scale. Working with hardware vendors and cloud providers continues to advance the state of the art, making confidential computing a practical choice for production workloads.
Deployment Strategies and Getting Started
Organizations evaluating confidential computing should consider their specific performance requirements alongside security needs. The technology has evolved from academic research to production-ready infrastructure that can handle the most demanding computational workloads while maintaining strict confidentiality requirements.
Assessment Phase: Start by identifying workloads that would benefit from confidential computing. These typically include applications handling sensitive data, multi-party collaboration scenarios, or workloads requiring regulatory compliance.
Pilot Implementation: Begin with a focused pilot deployment to understand the performance characteristics and operational requirements of confidential computing in your environment. For step-by-step guidance, see our guide on how to deploy confidential computing workloads.
Production Scaling: Once you’ve validated the approach, scale to production workloads using the rapid deployment capabilities of OpenMetal infrastructure. The ability to add servers to clusters in about 20 minutes supports agile scaling as requirements evolve.
For organizations ready to deploy confidential computing infrastructure, the combination of purpose-built hardware, GPU integration, and bare metal control provides the foundation for secure, high-performance computing at enterprise scale. To explore pricing and configuration options, visit our GPU servers and clusters pricing page.
The future of secure computing lies in technologies that don’t force trade-offs between security and performance. Confidential computing represents a fundamental shift in how we approach security for mission-critical workloads.
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