Architecting for Large Memory Workloads with OpenStack on OpenMetal

Memory-intensive applications are reshaping how organizations approach cloud infrastructure. Whether you’re running in-memory databases, training large-scale machine learning models, or processing vast datasets, the traditional approach of scaling out with many smaller instances often falls short when your workloads demand massive amounts of RAM.

If you’re an enterprise data architect or cloud infrastructure engineer, you understand the challenges of architecting systems that can handle workloads requiring hundreds of gigabytes or even terabytes of memory. Public cloud providers charge premium rates for high-memory instances, and those costs compound quickly when you’re dealing with persistent, memory-intensive applications.

OpenMetal’s approach to large memory workloads combines dedicated hardware with OpenStack’s flexibility, giving you the performance guarantees and cost predictability that enterprise applications demand. In this guide, we’ll explore how to architect, deploy, and optimize large memory workloads using OpenMetal’s high-memory server configurations.

Understanding Large Memory Workload Requirements

Before diving into architecture decisions, it’s important to understand what defines a memory-intensive workload and how to identify if your applications fall into this category.

Characteristics of Memory-Intensive Applications

RAM-intensive workloads share common characteristics that distinguish them from typical compute workloads. High memory utilization is the most obvious trait – these applications need significant memory to handle large datasets, often reaching hundreds of gigabytes or more in enterprise environments.

Real-time processing requirements also define many large memory applications. Live streaming platforms, multiplayer gaming systems, and financial trading applications demand rapid data access and processing, which places heavy reliance on both memory speed and volume. The ability to keep active datasets entirely in memory supports smooth user experiences with minimal lag.

Large dataset handling is another defining characteristic. Machine learning training, big data analytics, and scientific simulations often load entire datasets into memory for swift computation. Without sufficient memory, these datasets fall back to slower storage paths that significantly hinder performance.

Identifying Memory-Bound Workloads

You can confirm whether your workload is memory-bound through several symptoms and metrics. Fast symptom checks include high major page faults, steady swap activity, OOM kills in container logs, throughput plateaus while CPU utilization stays low, and long garbage collection pauses.

Key metrics to monitor include resident set size, page fault rates, cache hit ratios, swap in/out activity, memory bandwidth utilization, and garbage collection pause times. Tools like htop, vmstat, pidstat, and Kubernetes events can help you identify memory pressure before it impacts application performance.

However, before assuming you need more memory, rule out other potential bottlenecks. CPU saturation, slow storage paths, network latency, or GPU VRAM limits can sometimes mimic memory pressure symptoms.

OpenMetal’s High-Memory Server Architecture

OpenMetal’s v4 server lineup provides purpose-built infrastructure for large memory workloads, with configurations ranging from 256GB to 2TB of DDR5 RAM per server.

Server Configuration Options

The XXL v4 offers the peak memory capacity, featuring 2048GB of DDR5 RAM paired with dual 5th generation Intel Xeon Gold 6530 processors. Each processor provides 32 cores and 64 threads, delivering the compute power to match the massive memory capacity. This configuration targets the most demanding workloads like large-scale ML training requiring 1TB+ RAM nodes and complex financial modeling simulations.

The XL v4 provides 1024GB of DDR5 RAM with the same dual Intel Xeon Gold 6530 processor configuration. This setup offers an excellent balance for applications that need substantial memory but don’t require the full 2TB capacity of the XXL configuration.

For workloads requiring capable but more moderate memory allocations, the Large v4 includes 512GB of DDR5 RAM with dual Intel Xeon Gold 6526Y processors, while the Medium v4 offers 256GB of DDR5 RAM expandable to 4TB with dual Intel Xeon Silver 4510 processors.

Memory Architecture and Performance Features

All v4 servers utilize 8 memory channels per CPU socket, providing the bandwidth necessary for memory-intensive applications. This multi-channel configuration allows parallel access to memory, improving throughput during peak demand periods.

The servers support Intel SGX and TDX security features, with XL v4 and XXL v4 models meeting confidential computing requirements out of the box through their 8 DIMMs per CPU configuration and minimum 1TB RAM capacity. Medium v4 and Large v4 servers can be configured to support these features with memory upgrades or reconfigurations.

Storage performance complements the memory capacity through Micron 7450 MAX and Micron 7500 MAX NVMe drives. These high-performance SSDs provide fast spill paths when applications exceed RAM capacity and support the rapid I/O patterns common in memory-intensive workloads.

Intel Advanced Matrix Extensions (AMX) acceleration built into the processors enhances AI and machine learning performance, making the servers ideal for workloads that combine large memory requirements with intensive computation.

OpenStack Memory Management for Large Workloads

OpenStack provides several mechanisms to optimize memory allocation and performance for large memory applications, particularly through huge pages and NUMA topology management.

Configuring Huge Pages for Performance

OpenStack’s huge page feature provides important performance improvements for applications that are highly memory IO-bound. Standard 4KB pages create overhead when managing large amounts of memory, as the CPU must maintain many more page table entries. Huge pages reduce this overhead by using larger page sizes, typically 2MB or 1GB.

To use huge pages in your OpenStack deployment, you must first configure them at the host level. The number of persistent huge pages can be allocated at boot time to ensure availability, as memory fragmentation makes runtime allocation increasingly difficult for larger page sizes.

For memory-intensive applications, you can request huge pages through flavor extra specs or image metadata. Setting hw:mem_page_size=large in a flavor requests the largest available huge page size, while specific sizes can be requested with values like hw:mem_page_size=2MB or hw:mem_page_size=1GB.

NUMA Topology Considerations

Configuring huge pages for an instance implicitly results in NUMA topology configuration, which requires enabling the NUMATopologyFilter in Nova. This is actually beneficial for large memory workloads, as NUMA awareness helps optimize memory access patterns and reduce latency.

For applications with large working sets, NUMA topology becomes particularly important. Keeping memory local to the CPU that accesses it avoids remote memory penalties that can impact performance in multi-socket systems.

Memory Policy and Container Integration

When running containerized workloads on OpenStack, proper memory limits and requests become critical. Set realistic container memory limits with 20-30% headroom to prevent OOM kills, particularly for applications with variable memory usage patterns.

Container memory limits should align with the underlying instance’s memory allocation to avoid conflicts between the OpenStack scheduler and container orchestration platforms like Kubernetes.

Deploying Large Memory Applications on OpenMetal

OpenMetal’s infrastructure provides several advantages for deploying and managing large memory workloads beyond just the hardware specifications.

Rapid Deployment and Scaling

OpenMetal deploys servers as part of hyper-converged OpenStack-based private clouds, providing rapid scaling capabilities that address the agility requirements of memory-intensive workloads. New Cloud Cores can be deployed in 45 seconds, and additional servers can be added to existing clusters in approximately 20 minutes.

This rapid scaling capability addresses common pain points with traditional procurement cycles, where acquiring high-memory servers can take weeks or months. When your machine learning training runs require additional memory capacity or your in-memory database needs to scale, you can provision resources immediately rather than waiting for hardware procurement.

Storage Architecture for Memory-Intensive Workloads

The Ceph storage architecture underlying OpenMetal’s infrastructure provides triple replication for both block and object storage, ensuring fault tolerance and data durability for enterprise-scale memory-intensive jobs. This architecture supports both in-memory active datasets and persistent storage requirements.

For applications that spill data to storage or require checkpointing, the combination of high-performance NVMe storage with Ceph’s distributed architecture ensures consistent performance without creating storage bottlenecks.

Network Performance and Predictability

Network performance features 20Gbps private networking per server through dual 10Gbps NICs with unmetered east-west traffic. This configuration ensures predictable performance without surprise costs for memory-heavy distributed workloads that generate large amounts of internal data movement.

Many large memory applications, particularly distributed databases and machine learning training jobs, generate significant network traffic between nodes. The unmetered east-west traffic eliminates concerns about data transfer costs that can quickly escalate in public cloud environments.

Use Cases and Application Examples

Large memory configurations excel in specific application categories that benefit from keeping vast amounts of data in memory for rapid access and processing.

In-Memory Databases and Caching

SAP HANA deployments represent a prime use case for high-memory configurations. HANA loads entire datasets into memory for real-time analytics and can easily require 512GB to 2TB of RAM for enterprise deployments. Redis clusters also benefit significantly from large memory allocations, particularly when used for session storage, real-time analytics, or as a cache layer for high-traffic applications.

These applications demand not just capacity but also consistent performance. The dedicated hardware approach eliminates noisy neighbor issues that can impact database performance in multi-tenant public cloud environments.

Machine Learning and AI Training

Large-scale ML training workloads increasingly require 1TB+ RAM nodes to handle modern deep learning models and large datasets. Training large language models, computer vision models with high-resolution imagery, or recommendation systems with extensive feature sets all benefit from keeping training data entirely in memory.

The Intel AMX acceleration in OpenMetal’s v4 servers provides additional performance benefits for AI workloads, while the confidential computing capabilities through Intel TDX enable secure training on sensitive datasets.

Financial Modeling and Risk Analysis

Financial institutions run complex risk simulations and trading algorithms that require rapid access to market data and historical information. These applications often process terabytes of data and require consistent, predictable performance to meet regulatory requirements and trading deadlines.

The combination of large memory capacity, confidential computing features, and predictable performance makes OpenMetal’s infrastructure well-suited for financial workloads that cannot tolerate the performance variability common in shared public cloud environments.

Healthcare and Life Sciences

Healthcare datasets, particularly in genomics and medical imaging, require both large memory capacity and strong security controls. Processing whole genome sequences, medical imaging data, or conducting drug discovery simulations can require hundreds of gigabytes to terabytes of working memory.

Intel SGX and TDX provide hardware-based security for sensitive healthcare data, ensuring compliance with HIPAA and other healthcare regulations while maintaining the performance necessary for research and clinical applications.

Cost Optimization and Pricing Considerations

One of the primary advantages of OpenMetal’s approach to large memory workloads lies in the cost structure and pricing predictability compared to public cloud alternatives.

Fixed Monthly Pricing Model

OpenMetal operates under a fixed monthly pricing model that eliminates per-GB memory charges common in public cloud environments. This pricing approach provides significant cost advantages for persistent, memory-intensive workloads that run continuously rather than in short bursts.

Public cloud providers typically charge premium rates for high-memory instances, and those costs compound quickly for applications that require constant availability. The fixed pricing model allows for accurate budget planning without concerns about usage spikes driving up costs.

Avoiding Double-Payment During Migration

OpenMetal provides ramp pricing structures to avoid double-paying during migration periods. This approach helps enterprises running mission-critical memory-intensive applications transition from existing infrastructure without maintaining parallel systems at full cost.

Dedicated Hardware Cost Benefits

The dedicated hardware approach eliminates many of the hidden costs associated with public cloud memory-intensive workloads. You don’t pay for unused capacity in oversized instances, and the predictable performance eliminates the need to over-provision to account for noisy neighbor effects.

For applications with consistent memory requirements, the total cost of ownership typically favors dedicated infrastructure over public cloud alternatives, particularly when factoring in data transfer costs and the premium pricing for high-memory instances.

Security and Compliance for Sensitive Workloads

Large memory workloads often involve sensitive data that requires additional security controls beyond standard infrastructure protection.

Confidential Computing Capabilities

Intel TDX and SGX provide hardware-based security for sensitive data workloads including healthcare datasets, financial records, and blockchain validator nodes. These technologies create trusted execution environments that protect data even from privileged system access.

The confidential computing capabilities deliver both performance and compliance benefits for large memory applications that must meet strict regulatory requirements while maintaining high performance.

Data Center Security and Compliance

OpenMetal operates dedicated hardware in Tier III data centers located in Ashburn Virginia, Los Angeles California, Amsterdam Netherlands, and Singapore. These facilities provide physical security controls and compliance certifications necessary for enterprise workloads.

The geographic distribution allows you to place workloads close to users or comply with data residency requirements while maintaining consistent infrastructure characteristics across locations.

Monitoring and Performance Optimization

Successfully running large memory workloads requires ongoing monitoring and optimization to maintain peak performance and identify potential issues before they impact applications.

Memory Performance Metrics

Key metrics for monitoring large memory workloads include memory bandwidth utilization, NUMA remote access ratios, page fault rates, and garbage collection performance for managed runtime environments. These metrics help identify when applications approach memory capacity limits or experience performance degradation due to memory access patterns.

Application-Level Optimization

At the application level, several best practices improve memory efficiency and performance. Stream and batch large inputs instead of loading entire datasets simultaneously. Use columnar data formats and appropriate data types to reduce memory footprint. Implement buffer reuse patterns and tune cache bounds to prevent memory leaks.

For JVM-based applications, set appropriate heap sizes and garbage collection modes to fit latency goals. Python applications benefit from reducing temporary object creation and leveraging vectorized libraries for numerical operations.

Infrastructure Monitoring

Infrastructure monitoring should track swap usage, memory compression ratios, and container OOM events. Set up alerting for memory pressure indicators to prevent application failures and maintain performance SLAs.

Monitor storage performance metrics as well, since applications that exceed memory capacity will fall back to storage paths. NVMe performance becomes critical for applications that use memory-mapped files or require fast spill paths.

Migration and Deployment Strategies

Moving large memory workloads to new infrastructure requires careful planning to minimize downtime and ensure performance requirements are met.

Assessment and Planning

Begin with a thorough assessment of current memory usage patterns, peak requirements, and performance characteristics. Identify dependencies between components and plan migration sequencing to maintain application availability.

Use profiling tools to understand actual memory requirements versus allocated capacity. Many organizations discover they can optimize memory allocation during the migration process, potentially reducing costs while improving performance.

Phased Migration Approach

For critical applications, implement a phased migration approach that moves non-production environments first, followed by staged production migration. This approach allows validation of performance and configuration before moving business-critical workloads.

Validation and Testing

Conduct thorough performance testing after migration to validate that applications meet performance requirements in the new environment. Load testing should include peak usage scenarios and failure recovery testing to ensure reliability.

Getting Started with OpenMetal for Large Memory Workloads

OpenMetal provides engineer-assisted onboarding for complex deployments, helping ensure your large memory workloads are properly configured and optimized from the start.

The deployment process begins with understanding your specific requirements and recommending appropriate server configurations. OpenMetal’s engineering team can assist with OpenStack configuration, huge page setup, and application optimization to ensure you achieve optimal performance.

For organizations evaluating infrastructure options for large memory workloads, OpenMetal offers on-demand OpenStack cloud options that provide flexibility during the evaluation process.

Whether you’re running big data infrastructure, private AI applications, or seeking a public cloud alternative for memory-intensive workloads, OpenMetal’s high-memory configurations provide the performance, security, and cost predictability that enterprise applications demand.

Use the cloud deployment calculator to estimate costs for your specific requirements and see how OpenMetal’s approach compares to public cloud alternatives for your large memory workloads.

Wrapping Up: Large Memory Workloads on OpenStack

Large memory workloads require infrastructure that can provide consistent performance, predictable costs, and the security controls necessary for enterprise applications. OpenMetal’s combination of high-memory dedicated hardware, OpenStack flexibility, and fixed pricing delivers the foundation necessary for demanding memory-intensive applications.

By understanding your workload characteristics, properly configuring OpenStack memory management features, and leveraging OpenMetal’s high-performance infrastructure, you can achieve the performance and cost optimization that large memory applications demand while maintaining the security and compliance requirements of enterprise environments.