Use Cases for OpenMetal’s XL Hosted Private Cloud Hardware

Trying to figure out the best cloud hardware to match your needs? We’re here to help with our hosted private cloud use case series!

OpenMetal’s XL Hosted Private Cloud Hardware is engineered for performance. Featuring powerful Intel Xeon processors, plenty of DDR4/DDR5 memory, high-performance NVMe storage, and high-bandwidth networking on three hyperconverged servers running OpenStack + Ceph (referred to as the OpenMetal Private Cloud Core), the XL series is equipped to handle the most demanding and diverse business applications.

Per Server Specs:

XL v2

• Two Intel Xeon Gold 6338 32C/64T 2.0/3.2Ghz CPUs
• 1024GB DDR4 3200MHz RAM
• Four 3.2TB Intel DC-P4610 NVMe SSDs for storage with a 960GB boot disk
• 20Gbps private bandwidth, and 2Gbps public bandwidth included

Price: $5,616.00/month with no agreement or as low as $3,706.56/month with a 5 year agreement.

XL v2.1

• Two Intel Xeon Gold 6338 32C/64T 2.0/3.2Ghz CPUs
• 1024GB DDR4 3200MHz RAM
• Four 6.4TB Micron 7450 MAX NVMe SSDs for storage with a 960GB boot disk
• 20Gbps private bandwidth, and 2Gbps public bandwidth included

Price: $6,048.00/month with no agreement or as low as $3,991.68/month with a 5 year agreement. 

XL v3

• Two Intel Xeon Gold 6430 32C/64T 2.1/3.4Ghz CPUs
• 1024GB DDR5 4400MHz RAM
• Four 6.4TB Micron 7450 MAX NVMe SSDs for storage with a 960GB boot disk
• 20Gbps private bandwidth, and 2Gbps public bandwidth included

Price: $6,717.60/month with no agreement or as low as $4,433.62/month with a 5 year agreement.

XL v4

• Two Intel Xeon Gold 6530 32C/64T 2.1/4.0Ghz CPUs
• 1024GB DDR5 4800MHz RAM
• Four 6.4TB Micron 7450 MAX NVMe SSDs for storage with two 960GB boot disks
• 20Gbps private bandwidth, and 2Gbps public bandwidth included

Price: $6,717.60/month with no agreement or as low as $4,433.62/month with a 5 year agreement.

***Note that all prices are subject to change at any time. Prices shown above were gathered on December 19, 2024.

Key Advantages of OpenMetal’s XL Hosted Private Cloud Hardware

CPU Performance

• High Core Count: With 64 cores (32 cores per CPU) per server, the Intel Xeon Gold 6338, 6430, and 6530 processors provide excellent parallel processing capabilities. This is crucial for multi-threaded applications, virtualization, and workloads involving heavy multitasking.
• High Clock Speeds: The base and turbo frequencies of these CPUs (ranging from 2.0/3.2GHz to 2.1/4.0GHz) ensure strong single-threaded performance and quick response times for interactive applications. The 6530 in v4 stands out with the highest turbo frequency, making it ideal for latency-sensitive workloads.
• Modern Architecture: Based on Intel’s latest server architectures, these CPUs offer improved instructions per clock (IPC) and overall performance compared to previous generations.

Memory Capacity and Speed

• Large Memory Capacity: 1024GB (1TB) of RAM per server provides ample memory for large datasets, in-memory databases, and memory-intensive applications. This reduces reliance on slower storage and improves overall system responsiveness.
• High-Speed DDR4 and DDR5 Memory: DDR4 3200MHz (v2 and v2.1) and DDR5 4400MHz/4800MHz (v3 and v4) memory provides much higher bandwidth and lower latency compared to older memory technologies. DDR5 in v3 and v4 offers a noticeable performance advantage for memory-bound applications.

Storage Performance

• NVMe SSDs: The use of NVMe SSDs (Intel DC-P4610 or Micron 7450 MAX) delivers extremely high read/write speeds and low latency compared to traditional SATA SSDs or HDDs. This is a necessity for applications requiring fast data access, such as databases, virtualization, and high-performance computing.
• High Storage Capacity: With four NVMe drives per server (3.2TB or 6.4TB each), the XL configurations offer the needed storage capacity for demanding workloads. The higher capacity 6.4TB drives in v2.1, v3, and v4 are useful for data-intensive applications.
• Boot Disk Redundancy (v4): The inclusion of two 960GB boot disks in the v4 configuration adds a layer of redundancy, improving system reliability and uptime.

Networking

• High Bandwidth Connectivity: 20Gbps private bandwidth provides fast communication between servers within the private cloud, necessary for distributed applications and high-performance computing clusters. 2Gbps public bandwidth provides great connectivity to the outside world.

Hyperconverged Infrastructure (HCI)

• Integrated Compute, Storage, and Networking: The hyperconverged architecture simplifies management and deployment by combining compute, storage, and networking resources into a single platform. This increases efficiency and reduces complexity.
• OpenStack + Ceph: OpenStack for compute and Ceph for storage provides a powerful and flexible software-defined infrastructure. This allows for easy scaling, automation, and integration with other cloud platforms.

Considerations

Cost Sensitivity and Low Resource Requirements

• Lightly Used or Basic Web Applications: If your workloads consist primarily of static websites, low-traffic web applications, or simple development/testing environments with minimal resource demands, the XL series may be overkill. More cost-effective solutions like shared hosting, VPS, or OpenMetal’s smaller hardware configurations are likely more appropriate.
• Infrequent or Burst Workloads: If your application sees infrequent spikes in demand and remains mostly idle, a cloud-based solution with auto-scaling capabilities might be more cost-efficient. You’d only pay for the resources consumed during peak usage.

Specific Hardware Needs

• GPU-Intensive Workloads: The XL configurations, while powerful, do not include dedicated GPUs. If your applications rely heavily on GPU acceleration (e.g., deep learning training, complex simulations, video rendering), you would need to explore solutions with dedicated GPU servers or consider adding GPU instances to your OpenStack environment.
• Specialized Hardware Requirements: If your application requires very specific hardware configurations not offered by the XL series (e.g., FPGAs, specialized network cards, large amounts of persistent memory), you’d need to look for alternative solutions.

Data Residency and Compliance

• Strict Data Sovereignty Requirements: If your data must reside within a specific geographic location not served by OpenMetal’s data centers, you’d want to find a provider with a presence in that region.
• Highly Regulated Industries with Specific Compliance Needs: While OpenMetal offers secure and compliant infrastructure, certain highly regulated industries (e.g., some aspects of healthcare or government) may have very specific compliance requirements that need careful evaluation. Confirm if OpenMetal’s infrastructure meets those specific needs.

Management Preference

• Fully Managed Solutions: If you prefer a fully managed solution where the provider handles all aspects of infrastructure management, including OS patching, security updates, and monitoring, a managed hosting or cloud provider might be a better fit. While OpenMetal offers some managed services, the core offering is focused on handling the hardware while you handle everything else.

Existing Infrastructure and Integration

• Complex On-Premise Infrastructure: If you have very complex on-premises infrastructure with tight integrations and dependencies, migrating to a hosted private cloud might require significant effort and planning. A hybrid cloud strategy or a phased migration approach might be worth looking into.

Real-World Use Cases Powering Innovation with OpenMetal XL Hardware

OpenMetal’s XL Hosted Private Cloud Hardware is currently powering businesses of all sizes and industries. Here are a few real-life examples showcasing the versatility and impact of the XL series:

• Leading Efficiency for Accounting Software: A prominent energy industry accounting software company utilizes XL hardware in their production environment. OpenMetal’s private cloud solution allows them to achieve significant cost savings – exceeding 50% compared to their previous public cloud provider (AWS) – while maintaining complete separation between customer environments. This not only boosts security, but supports new possibilities for efficiency within their infrastructure. They’re also exploring the XL v4 tier for potential big data needs, demonstrating the possibilities available within the XL series.
• Academic Powerhouse Optimizes Research Capabilities: Howard University, a respected educational institution, migrated from Google Cloud Platform (GCP) to OpenMetal XL hardware, gaining substantial savings and improved efficiency. Their research activities, encompassing clinical and genomic research alongside accounting and other departmental needs, generate a significant amount of data. Using XL v2 hardware provides them with the robust platform needed to support these demanding workloads.
• Cloud Hosting Providers Turn to XLs for Differentiation and Growth: Several cloud hosting companies, like Convesio, have chosen the XL series to power their internal infrastructure and provide high-caliber hosting services to their customers. Convesio’s move from public cloud to OpenMetal demonstrates the strategic advantages of dedicated resources and control. Their success story highlights how XL hardware empowers hosting providers to differentiate themselves, achieve cost savings, and deliver greater efficiency and flexibility to their customers. Read Convesio’s story

High-Frequency Trading (HFT)

Most suitable hardware: XL v4

HFT is characterized by extremely high transaction volumes, ultra-low latency requirements, and the need to process huge amounts of market data in real-time. Key performance factors include:

• Low Latency: Minimizing the time it takes to receive market data, process it, and execute trades is one of the most important requirements. Even milliseconds of delay can result in lost opportunities.
• High Throughput: The system must be able to handle a large number of orders and market data updates per second.
• Deterministic Performance: Consistent and predictable performance is crucial. Variations in latency or processing time can lead to unpredictable trading operations.

Why XL Hardware for HFT

CPU Performance (Critical for Algorithmic Trading)

• High Clock Speeds (especially the 6530 in v4): The higher clock speeds, particularly the turbo frequencies up to 4.0 GHz in the v4 configuration, are essential for executing complex trading algorithms quickly. These algorithms often involve intricate calculations and require fast single-threaded performance. This is where the 6530’s higher clock speed provides an advantage.
• High Core Count: While single-threaded performance is crucial for core trading logic, the high core count (64 cores per server) allows for parallel processing of multiple market data feeds, running multiple trading strategies simultaneously, and handling pre- and post-trade processing tasks.

Memory Performance (For In-Memory Data Processing)

• Large Memory Capacity (1TB): HFT systems often rely on in-memory databases and caching to store and access market data quickly. The large RAM capacity of the XL configurations allows for keeping large datasets in memory, minimizing latency associated with disk access.
• High-Speed DDR5 Memory (v3 and v4): The significantly higher bandwidth and lower latency of DDR5 memory compared to DDR4 further reduce data access times and improve overall system responsiveness. This is particularly important for handling high-velocity market data streams.

Storage Performance (For Historical Data and Logging)

• NVMe SSDs: The use of NVMe SSDs provides very low latency and high throughput for reading and writing data. This is essential for storing historical market data, logging trades, and quickly accessing data for analysis and backtesting.

Networking (Minimizing Network Latency)

• High Bandwidth Private Network (20Gbps): The high-bandwidth private network allows for fast communication between servers within the private cloud environment. This is needed for distributing workloads, sharing data between trading algorithms, and coordinating trading activities across multiple servers. While the 2Gbps public bandwidth is not the primary concern for internal HFT communications, it is still a significant amount of bandwidth for external market data feeds.
• Low Latency Connectivity: Proximity to exchanges and direct connectivity options can further reduce latency, giving HFT firms a competitive edge. This is something we’re happy to discuss with you at OpenMetal so you can learn more about our specific data center locations and network connectivity options.

Hosted Private Cloud’s Benefits

• Dedicated Hardware: Unlike public cloud environments where resources are shared, a hosted private cloud provides dedicated hardware. This leads to consistent performance and minimized risk of resource contention affecting trading execution.
• Control and Customization: A hosted private cloud offers greater control over the hardware and software environment, allowing HFT firms to optimize systems for maximum performance and security.

In summary: The XL hardware, especially the v4 configuration with its high clock speeds and DDR5 memory, combined with a dedicated hosted private cloud, provides a strong foundation for building high-performance, low-latency HFT systems. The dedicated hardware and high bandwidth private networking are important differentiators from public cloud options for this use case.

AI/ML Model Training and Inference

Most suitable hardware: XL v4

AI/ML workloads, especially deep learning, are computationally intensive and require a lot of resources. Key performance factors include:

• Computational Power: Training complex models requires massive parallel processing capabilities, particularly for deep learning models with millions or billions of parameters.
• Memory Capacity and Bandwidth: Large datasets and complex models require a lot of memory to avoid performance bottlenecks. High memory bandwidth is important for quickly transferring data between the CPU and memory.
• Storage Performance: Fast storage is needed for loading and saving datasets, model checkpoints, and other data related to the training process.

Why XL Hardware for AI/ML

CPU Performance (For Data Preprocessing, Feature Engineering, and Model Inference)

• High Core Count: The high core count (64 cores per server) is beneficial for data preprocessing tasks, feature engineering, and running multiple training experiments concurrently. While GPUs are the primary workhorses for deep learning training, CPUs still play a big role in these supporting tasks.
• High Clock Speeds: The higher clock speeds are useful for tasks that are not easily parallelized or for inference workloads where low latency is important.

Memory Performance (Crucial for Large Datasets and Models)

• Large Memory Capacity (1TB): The generous RAM capacity is ideal for handling large datasets and complex models in memory. This reduces the need for frequent disk access, which can slow down training.
• High-Speed DDR5 Memory (v3 and v4): The increased bandwidth and lower latency of DDR5 memory are particularly beneficial for AI/ML workloads, allowing for faster data transfer between the CPU and memory. This is critical for training large models and processing high-volume data streams during inference.

Storage Performance (For Efficient Data Access)

• NVMe SSDs: The NVMe drives provide the high read/write speeds and low latency needed for efficient data access during training and inference. This is important for loading large datasets quickly and saving model checkpoints frequently.

Hosted Private Cloud’s Benefits

• Dedicated Resources: Dedicated hardware ensures consistent performance and avoids resource contention with other users. This is especially needed for long-running training jobs.
• Control and Customization: A hosted private cloud offers greater control over the hardware and software environment, allowing for customization and optimization for specific AI/ML workloads.
• Scalability: The ability to easily add more servers or upgrade to higher-performance configurations provides the scalability needed for growing AI/ML projects.

Specific AI/ML Use Cases and How XL Hardware Excels

• Natural Language Processing (NLP): Training large language models (LLMs) requires massive computational resources and memory. The XL hardware’s high core count, large RAM, and fast storage provide a perfect platform for training and fine-tuning these models.
• Computer Vision: Training image recognition and object detection models involves processing large image datasets. The XL hardware’s high memory capacity and fast storage are great for handling these data-intensive tasks.
• Recommendation Systems: Building and training recommendation systems often involves processing large user-item interaction datasets. The XL hardware’s high core count and large RAM are beneficial for these workloads.

Inference Considerations

While GPUs are often preferred for high-throughput inference, the XL hardware can be suitable for certain inference scenarios, especially when:

• Model Size is Relatively Small: For smaller models, CPUs can provide sufficient performance for inference.
• Latency Requirements are Moderate: If ultra-low latency is not a requirement, CPUs can be a cost-effective option for inference.
• CPU-Optimized Frameworks are Used: Using frameworks and libraries optimized for CPU inference can improve performance.

In summary: OpenMetal’s XL hardware provides a strong foundation for many AI/ML workloads. It’s a great fit for data preprocessing, feature engineering, training moderately sized models, and certain inference scenarios in particular. The high core count, large RAM, and fast storage are definite advantages. For extremely large models or very high-throughput inference, combining XL with dedicated GPU instances would be the ideal solution.

Game Development and Hosting

Most suitable hardware: XL v2, v2.1, or v3 for game development (v3 is ideal for intensive projects with complex physics simulations or advanced graphics); XL v3 or v4 for hosting depending on how demanding the game is and the number of players.

Game development and hosting encompass a wide range of resource-intensive tasks:

Game Development

• Compilation and Building: Compiling game code and building game assets requires a great deal of CPU power and memory.
• Testing and Iteration: Running game builds for testing and iteration demands consistent performance and low latency.
• Version Control and Collaboration: Managing large game projects involves lots of storage and network bandwidth for efficient collaboration.

Game Server Hosting

• Real-time Performance: Game servers must handle a large number of concurrent players with minimal latency to provide a smooth and responsive gaming experience.
• Network Bandwidth: High network bandwidth is essential for transmitting game data between players and the server.
• Scalability: Game servers need to be able to scale to accommodate fluctuating player populations.

Why XL Hardware for Game Development and Hosting

CPU Performance (For Compilation, Game Logic, and Physics Simulations)

• High Core Count: The high core count (64 cores per server) is beneficial for compiling large game projects, running complex game logic, and handling physics simulations.
• High Clock Speeds: Higher clock speeds are important for executing game logic efficiently and providing responsive gameplay. This is particularly relevant for game servers that need to process player actions quickly.

Memory Performance (For Game Assets and Server Processes)

• Large Memory Capacity (1TB): The large RAM capacity allows for storing large game assets, running multiple game server instances, and handling memory-intensive game processes.
• High-Speed DDR5 Memory (v3 and v4): The increased bandwidth and lower latency of DDR5 memory are beneficial for quickly accessing game assets and processing game data.

Storage Performance (For Game Assets, Builds, and Server Data)

• NVMe SSDs: The NVMe drives provide the high read/write speeds and low latency needed for loading game assets quickly, storing game builds, and accessing server data.

Networking (For Low-Latency Gameplay)

• High Bandwidth Private Network (20Gbps): The high-bandwidth private network allows for fast communication between development servers, build servers, and game servers. This is helpful for collaborative development and low-latency gameplay. The 2Gbps public bandwidth provides ample capacity for player connections.

Hosted Private Cloud’s Benefits

• Dedicated Resources: Dedicated hardware ensures consistent performance and avoids resource contention – critical for smooth gameplay and efficient development workflows.
• Control and Customization: A hosted private cloud offers greater control over the hardware and software environment, allowing developers to optimize their systems for specific game engines and requirements.
• Scalability: The ability to easily scale resources by adding more servers or upgrading configurations is necessary for handling fluctuating player populations and growing development projects.

Specific Game Development and Hosting Use Cases and How XL Excels

• Multiplayer Game Servers: Hosting online multiplayer games requires robust servers that can handle a large number of concurrent players with minimal latency. The XL hardware’s high core count, large RAM, and high bandwidth networking make it well-suited for this purpose.
• Game Development and Testing: The XL hardware provides a powerful platform for game developers to compile, build, and test their games. The high core count and large RAM accelerate compilation times, while the fast storage ensures quick loading of game assets.
• Game Streaming: While not directly related to development, the XL hardware, especially with its high bandwidth networking, could support game streaming services. This would require additional software and configuration.

In summary: OpenMetal’s XL hardware provides a powerful and versatile platform for game development and hosting. The high core count, large RAM, fast storage, and high bandwidth networking are key advantages for these demanding workloads. The dedicated nature of the hosted private cloud ensures consistent performance and control, making it a solid choice for game developers and server providers.

3D Rendering and Visual Effects

Most suitable hardware: XL v3 or v4 depending on budget and specific software requirements.

3D rendering and VFX are extremely computationally intensive, requiring significant resources:

• CPU Rendering: Many rendering engines rely heavily on CPUs for processing complex scenes, especially for tasks like ray tracing, path tracing, and global illumination.
• Memory Capacity: Large and complex scenes with high-resolution textures and complex geometry require substantial memory to avoid swapping to disk, which can drastically slow down rendering times.
• Storage Performance: Fast storage is crucial for loading scene files, textures, and other assets, as well as for saving rendered frames and output files.
• Network Bandwidth: For collaborative workflows and distributed rendering, high network bandwidth is needed for transferring large files and coordinating rendering tasks across multiple machines.

Why XL Hardware for 3D Rendering and VFX

CPU Performance (The Workhorse of Rendering)

• High Core Count: The high core count (64 cores per server) is extremely beneficial for CPU rendering. Rendering tasks are highly parallelizable, meaning that they can be efficiently distributed across multiple cores. The more cores available, the faster the rendering process.
• High Clock Speeds: While core count is generally more important than clock speed for rendering, higher clock speeds can still provide a performance boost. This is especially true for tasks that are not perfectly parallelizable or for interactive tasks within 3D applications.

Memory Performance (Preventing Bottlenecks)

• Large Memory Capacity (1TB): The large RAM capacity is crucial for handling large and complex scenes. Insufficient memory will force the system to use disk space as virtual memory, which is much slower and can lead to extremely long rendering times.
• High-Speed DDR5 Memory (v3 and v4): The higher bandwidth and lower latency of DDR5 memory are beneficial for quickly accessing and processing scene data, further improving rendering performance.

Storage Performance (For Efficient Asset Loading and Saving)

• NVMe SSDs: The NVMe drives provide the high read/write speeds and low latency needed for loading scene files, textures, and other assets quickly. This is especially important for large scenes with high-resolution textures.

Networking (For Distributed Rendering and Collaboration)

• High Bandwidth Private Network (20Gbps): The high-bandwidth private network is essential for distributed rendering, where a single scene is rendered across multiple machines. It allows for fast transfer of scene data and rendered frames between servers.

Hosted Private Cloud’s Benefits

• Dedicated Resources: Dedicated hardware ensures consistent performance and avoids resource contention, which is crucial for long rendering jobs.
• Scalability: The ability to easily add more servers to a render farm or upgrade configurations provides scalability for large projects.
• Control and Customization: A hosted private cloud offers greater control over the hardware and software environment, allowing studios to optimize their systems for specific rendering engines and workflows.

Specific 3D Rendering and VFX Use Cases and How XL Excels

• Offline Rendering: Rendering high-quality images and animations for film, television, and advertising. The XL hardware’s high core count and large RAM are ideal for this type of workload.
• Visual Effects Production: Creating visual effects for movies, television shows, and video games. The XL hardware’s performance is suited for handling complex simulations, compositing, and other VFX tasks.
• Architectural Visualization: Rendering realistic visualizations of buildings and interiors. The XL hardware can handle the large and detailed scenes often used in architectural visualization.

In summary: OpenMetal’s XL hardware, particularly the v3 and v4 tiers, provides a powerful and scalable platform for 3D rendering and VFX production. The high core count, large RAM, fast storage, and high bandwidth networking are big advantages for these demanding workloads. Dedicated hosted private clouds provide consistent performance and control, making them a strong choice for studios and individual artists.

Software Development and Testing

Most suitable hardware: XL v2.1 (the large storage is beneficial if you have very large repositories or build artifacts), v3 (the faster memory is great for very large projects and demanding compilation processes), or v4 (most appropriate for extremely demanding workloads, such as developing and testing very large and complex applications, running extensive simulations, or requiring the absolute lowest latency for testing).

Modern software development and testing practices place high demands on infrastructure:

• Compilation and Building: Compiling large codebases, especially in languages like C++ or Java, requires a great deal of CPU power and memory.
• Testing (Unit, Integration, and End-to-End): Running various types of tests, including unit tests, integration tests, and end-to-end tests, can be resource-intensive, especially for complex applications.
• Continuous Integration/Continuous Deployment (CI/CD): CI/CD pipelines automate the build, test, and deployment process, requiring consistent performance and fast turnaround times.
• Virtualization and Containerization: Developers often use virtual machines (VMs) or containers to create isolated environments for development and testing. This requires sufficient resources to run multiple VMs or containers concurrently.
• Version Control and Collaboration: Managing large code repositories and collaborating with multiple developers requires considerable storage and network bandwidth.

How XL Hardware Addresses Software Development and Testing Needs

CPU Performance (For Compilation, Testing, and Running Multiple Environments)

• High Core Count: The high core count (64 cores per server) is highly beneficial for compiling large codebases and running multiple tests in parallel. This significantly reduces build and test times.
• High Clock Speeds: Higher clock speeds improve the performance of individual tasks within the development and testing process, such as running unit tests or executing single-threaded code.

Memory Performance (For Running Multiple Environments and Handling Large Projects)

• Large Memory Capacity (1TB): The generous RAM capacity allows developers to run multiple VMs or containers concurrently, each with its own dedicated memory. This is crucial for testing applications in different environments or for running multiple development branches simultaneously.
• High-Speed DDR5 Memory (v3 and v4): The increased bandwidth and lower latency of DDR5 memory improve the performance of memory-intensive tasks, such as compiling large codebases and running in-memory databases for testing.

Storage Performance (For Fast Access to Code, Dependencies, and Test Data)

• NVMe SSDs: The NVMe drives provide the high read/write speeds and low latency needed for fast access to code repositories, build artifacts, dependencies, and test data. This significantly speeds up build and test processes.

Networking (For Collaboration and CI/CD Pipelines)

• High Bandwidth Private Network (20Gbps): The high-bandwidth private network allows for fast communication between development servers, build servers, and testing environments. This is crucial for efficient collaboration and smooth operation of CI/CD pipelines.

Hosted Private Cloud’s Benefits

• Dedicated Resources: Dedicated hardware ensures consistent performance and avoids resource contention – necessary for maintaining stable development and testing environments.
• Control and Customization: A hosted private cloud offers greater control over the hardware and software environment, allowing developers to configure their systems to meet specific project needs.
• Security: A hosted private cloud provides a more secure environment for storing sensitive code and data compared to public cloud environments.

Specific Software Development and Testing Use Cases and How XL Excels

• Large-Scale Software Development: Developing and testing large and complex software applications with demanding resource requirements. The XL hardware provides the necessary resources to handle these workloads.
• Mobile App Development: Building and testing mobile apps for various platforms requires emulating different devices and operating systems. The XL hardware can easily handle multiple emulators at the same time.
• Game Development (Covered Above): As discussed earlier, the XL hardware is also ideal for game development, which involves significant software development and testing.
• CI/CD Pipelines: Running automated build, test, and deployment pipelines. The XL hardware’s performance ensures fast turnaround times for these pipelines.

In summary: OpenMetal’s XL hardware provides a powerful and versatile platform for software development and testing. The high core count, large RAM, fast storage, and high bandwidth networking are key advantages for these demanding workloads. A dedicated hosted private cloud also ensures consistent performance, control, and security, making it a good fit for development teams of all sizes.

How to Get Started on an OpenMetal Large Hosted Private Cloud

The OpenMetal XL Hosted Private Cloud Hardware delivers a powerful and versatile platform built for demanding workloads. The XL series provides the dedicated resources, high performance, and granular control necessary to power everything from high-frequency trading and AI/ML model training to game development, 3D rendering, and complex software development. With its high core count CPUs, massive memory capacity, lightning-fast storage, and high-bandwidth networking, the XL series offers a great solution for organizations seeking uncompromising performance and control.