Puppet
Server Configuration Profile: Puppet (High-Density Compute Node)
This document provides a comprehensive technical specification and operational guide for the "Puppet" server configuration. The Puppet profile is designed as a high-density, dual-socket compute node optimized for virtualization density, in-memory databases, and scale-out analytics workloads requiring rapid data access and significant core counts.
1. Hardware Specifications
The Puppet configuration emphasizes maximizing core count, memory bandwidth, and NVMe storage performance within a standardized 2U rackmount form factor. This configuration relies on the latest generation of server platforms supporting PCIe Gen 5.0 and high-speed interconnects.
1.1. Chassis and Platform
The base platform utilizes a high-airflow, 2U chassis designed for dense deployment in enterprise data centers adhering to standard rack elevations.
| Component | Specification | Notes |
|---|---|---|
| Form Factor | 2U Rackmount | Optimized for density and airflow management. |
| Motherboard / Chipset | Dual-Socket (e.g., Intel C741 or AMD SP5 Platform) | Supports dual-CPU configurations via proprietary backplane. |
| Power Supply Units (PSUs) | 2x 2000W Redundant (N+1) Titanium Efficiency | Required for peak CPU/GPU load. Requires 20A circuit capacity. |
| Cooling Solution | High-Static Pressure Fans (7x Hot-Swap, Redundant) | Optimized for dense component cooling; supports ambient temperatures up to 35°C (95°F). |
| Management Interface | Dedicated IPMI/BMC (e.g., ASPEED AST2600) | Supports remote KVM, power cycling, and firmware updates (Server Management Protocols). |
1.2. Central Processing Units (CPUs)
The Puppet configuration mandates dual-socket deployment utilizing processors optimized for high core count and large L3 cache structures, crucial for minimizing memory latency in complex workloads.
| Parameter | Specification (Example: Intel Xeon Scalable 4th Gen equivalent) | Specification (Example: AMD EPYC 9004 Series equivalent) |
|---|---|---|
| CPU Sockets | 2 | 2 |
| Model Target | Xeon Platinum 8480+ (or higher) | EPYC 9654 (or higher) |
| Core Count (Per CPU) | 56 Cores (112 Total) | 96 Cores (192 Total) |
| Thread Count (Total) | 224 Threads | 384 Threads |
| Base Clock Frequency | 2.2 GHz | 2.4 GHz |
| Max Turbo Frequency (Single Core) | Up to 3.8 GHz | Up to 3.7 GHz |
| L3 Cache (Total) | 112 MB (224 MB Total) | 384 MB (768 MB Total) |
| TDP (Per CPU) | 350W | 360W |
| Memory Channels Supported | 8 Channels DDR5 | 12 Channels DDR5 |
- Note: The AMD configuration generally offers a higher total thread count and significantly larger L3 cache, impacting performance in memory-bound applications compared to the Intel counterpart.*
1.3. Memory Subsystem (RAM)
Memory capacity and speed are critical differentiators for the Puppet profile, often used for in-memory caches or large dataset processing. The configuration utilizes the maximum supported DIMM slots per socket, prioritizing speed (e.g., DDR5-4800 or DDR5-5200) and maintaining optimal channel population for maximum bandwidth.
| Parameter | Specification | Rationale |
|---|---|---|
| Memory Type | DDR5 RDIMM (ECC Registered) | Required for server stability and error correction. |
| Total Capacity (Minimum) | 1024 GB (1 TB) | Base configuration for substantial virtualization hosts. |
| Total Capacity (Maximum) | 8192 GB (8 TB) | Achieved via 32x 256GB DIMMs (assuming 32 DIMM slots). |
| DIMM Speed Target | DDR5-5200 MHz (or highest stable speed) | Maximizes data throughput to the CPU. (DDR5 Technology) |
| Memory Configuration | Fully Populated Channels (e.g., 16x 64GB DIMMs per CPU) | Ensures optimal memory controller performance and bandwidth utilization. |
1.4. Storage Architecture
The storage subsystem is optimized for high IOPS and low latency, relying exclusively on NVMe devices connected via PCIe Gen 5.0 lanes, bypassing slower SATA/SAS controllers where possible.
| Component | Specification | Quantity |
|---|---|---|
| Boot Drive (OS/Hypervisor) | 2x 960GB M.2 NVMe (U.2/PCIe Add-in Card) | 2 (Configured in RAID 1 via onboard HW RAID or BIOS settings) |
| Storage Class | Enterprise NVMe (High Endurance) | Focus on sustained write performance. |
| Component | Specification | Quantity | Total Capacity (Approximate) |
|---|---|---|---|
| Primary Data Storage | 8x 7.68TB U.2/E3.S NVMe SSDs | 8 | 61.44 TB Usable (RAID 10 or ZFS equivalent) |
| PCIe Lanes Allocation | Dedicated PCIe Gen 5.0 x16 or x8 lanes per group of 4 drives | Ensures full bandwidth utilization for all NVMe devices. (PCIe Architecture) | |
| Optional Secondary Storage | 2x High-Speed SAS SSDs (for management/logs) | 2 | 3.2 TB (Optional) |
1.5. Networking and Expansion
The Puppet configuration requires high-speed, low-latency networking to support cluster interconnects, storage traffic (if using external SAN/NAS), and rapid data ingress/egress.
| Component | Specification | Quantity |
|---|---|---|
| Base LAN (Management/IPMI) | 1GbE (Dedicated) | 1 |
| Data Network Interface (LOM/OCP) | 2x 25GbE SFP28 or 2x 100GbE QSFP28 | 1 Module (Configurable) |
| PCIe Expansion Slots | 4x PCIe Gen 5.0 x16 full height/length slots | 4 (Requires careful slot selection based on CPU lane availability) |
| Optional Accelerator Support | 1x Dual-Width GPU (e.g., NVIDIA H100 or equivalent) | 1 (If required for AI/ML inference; reduces available storage slots) |
2. Performance Characteristics
The Puppet configuration is engineered for peak throughput, significantly exceeding standard general-purpose servers due to the high core density and exclusive use of high-speed I/O. Performance validation focuses on throughput under sustained load.
2.1. CPU Performance Metrics
Given the high core count, sustained performance under multi-threaded workloads is the primary metric.
| Benchmark | Metric | Result (Dual Socket) | Comparison Baseline (2-Socket Previous Gen) |
|---|---|---|---|
| SPECrate 2017_int_base | Integer Throughput Score | ~1250 | +45% improvement |
| SPECrate 2017_fp_base | Floating Point Throughput Score | ~1100 | +55% improvement |
| Memory Bandwidth (Aggregate) | Read/Write Peak (GB/s) | ~800 GB/s | Dependent on DIMM configuration and channel population. |
| Inter-CPU Latency (NUMA Hop) | Average Latency (Nanoseconds) | < 100 ns (via UPI/Infinity Fabric) | Critical for tightly coupled parallel processes. |
- Reference: Detailed benchmark data is typically found in official processor specification documents from Intel/AMD and should be cross-referenced with specific BIOS/UEFI settings.* (NUMA Architecture)
2.2. Storage I/O Performance
The reliance on PCIe Gen 5.0 NVMe storage provides exceptionally low latency, crucial for database transaction processing (OLTP) and data streaming.
| Metric | Specification | Notes |
|---|---|---|
| Sequential Read Throughput | > 40 GB/s | Achievable sustained read rate for large file transfers. |
| Sequential Write Throughput | > 30 GB/s | Indicates strong endurance write performance under load. |
| Random Read IOPS (4K QD32) | > 15 Million IOPS | Primary metric for high-transaction databases. |
| Random Write IOPS (4K QD32) | > 7 Million IOPS | Demonstrates capacity for handling heavy logging and indexing. |
| Average Latency (Read) | < 40 microseconds (µs) | Essential for minimizing application response times. |
2.3. Virtualization Density
The combination of high core count (up to 384 threads) and massive memory capacity (up to 8TB) allows the Puppet configuration to host a very high density of virtual machines (VMs) or containers.
- **VM Density Target:** For standard enterprise workloads (e.g., 4 vCPU, 16GB RAM per VM), this configuration can comfortably support **64 to 96 heavily provisioned VMs** while maintaining acceptable resource overcommitment ratios (e.g., 3:1).
- **Container Density Target:** Utilizing lightweight container runtimes (e.g., Kubernetes), the density can increase significantly, potentially exceeding **500 application pods** depending on the resource profile of the individual containers. (Containerization Technologies)
3. Recommended Use Cases
The Puppet configuration is not intended for general-purpose file serving or low-utilization environments. Its high component density and premium pricing mandate deployment in performance-critical, resource-intensive roles.
3.1. High-Performance Computing (HPC) and Simulation
The large number of physical cores and high aggregate memory bandwidth make this platform ideal for tightly coupled parallel processing tasks where communication latency between nodes is managed effectively (e.g., using high-speed InfiniBand or RoCE interconnects via the expansion slots).
- **MPI Workloads:** Executing Message Passing Interface (MPI) jobs that require extensive inter-process communication.
- **Fluid Dynamics and Finite Element Analysis (FEA):** Simulations that benefit from massive parallelism and large working datasets held in memory. (HPC Cluster Design)
3.2. Large-Scale Virtualization and Cloud Infrastructure
As a high-density hypervisor host, Puppet maximizes return on rack space and power draw by consolidating numerous virtual workloads onto fewer physical machines.
- **Enterprise VDI (Virtual Desktop Infrastructure):** Hosting hundreds of concurrent users requiring high I/O responsiveness.
- **Private Cloud Infrastructure:** Serving as core compute nodes within OpenStack or VMware Cloud Foundation deployments, especially for compute-intensive tenants. (Virtualization Best Practices)
3.3. In-Memory Databases and Real-Time Analytics
The 8TB memory ceiling, combined with extremely fast NVMe storage, positions Puppet perfectly for workloads that must operate entirely in RAM or require instant access to massive datasets.
- **SAP HANA Deployments:** Meeting the high RAM requirements for large production instances. (SAP HANA Sizing Guide)
- **Real-Time Data Warehousing:** Hosting operational data stores (ODS) or high-speed OLAP cubes that demand sub-millisecond query response times. (Data Warehousing Architectures)
3.4. Machine Learning (ML) Training and Inference
While specialized GPU servers are often preferred for pure deep learning training, the Puppet configuration excels in two related areas:
1. **Data Pre-processing Pipelines:** Utilizing the high core count and fast NVMe storage to rapidly prepare, clean, and augment datasets before they are fed to dedicated accelerators. 2. **Inference Serving:** Deploying complex models (especially large language models requiring significant memory) where the CPU architecture provides sufficient throughput for serving concurrent user requests. (GPU Acceleration in Servers)
4. Comparison with Similar Configurations
To contextualize the Puppet configuration, it is useful to compare it against two common alternatives: the **"Atlas" (Storage Density Node)** and the **"Sentinel" (Single-Socket/Edge Node)**.
- **Puppet (High-Density Compute):** Balanced high core count, massive RAM, fast local NVMe. High power draw, high cost, low serviceability footprint.
- **Atlas (Storage Density Node):** Optimized for maximum drive count (e.g., 36+ SAS/SATA drives in 2U/4U), lower core/RAM density. Ideal for Ceph/Gluster storage or large archival needs.
- **Sentinel (Single-Socket/Edge Node):** Lower total core count (e.g., 128 cores max), limited RAM (e.g., 2TB max), often uses lower TDP processors. Ideal for edge deployments or smaller, isolated applications where cost per socket is prioritized over aggregate performance. (Server TCO Modeling)
4.1. Comparative Feature Matrix
| Feature | Puppet (High-Density Compute) | Atlas (Storage Density) | Sentinel (Edge Compute) |
|---|---|---|---|
| Form Factor | 2U | 4U (Typical) | 1U or 2U |
| Max Cores (Total) | 192 (AMD EPYC) | 128 (Mid-range Xeon) | 64 (Mid-range EPYC) |
| Max RAM Capacity | 8 TB | 2 TB | 2 TB |
| Primary Storage Medium | 8-12x NVMe (U.2/E3.S) | 24-36x SAS/SATA HDDs/SSDs | |
| PCIe Gen | Gen 5.0 | Gen 4.0 (Cost optimized) | Gen 5.0 |
| Typical Power Draw (Peak) | 3000W - 4000W | 1800W - 2500W | 1200W - 1800W |
| Best Suited For | Virtualization, In-Memory DBs, HPC | Software-Defined Storage (SDS), Archive | Distributed Caching, Microservices, Edge AI |
4.2. Performance Trade-offs
The key trade-off when selecting Puppet over other configurations lies in I/O flexibility versus raw compute power.
- **Puppet vs. Atlas:** Puppet offers vastly superior CPU performance (up to 60% more cores) and memory bandwidth, but Atlas can support 3-4 times the raw spinning disk capacity, making it better for cold storage or scale-out file systems where latency is tolerable. (Storage Area Networks vs. Direct Attached Storage)
- **Puppet vs. Sentinel:** Puppet provides nearly double the compute capacity and double the memory capacity of the Sentinel, justifying its deployment in centralized, Tier-0 environments where downtime or performance degradation is unacceptable. Sentinel offers better cost efficiency for highly distributed, independent workloads. (Server Consolidation Strategies)
5. Maintenance Considerations
The density and power requirements of the Puppet configuration necessitate stringent operational procedures regarding power infrastructure, thermal management, and component replacement.
5.1. Power Infrastructure Requirements
Due to the high TDP components (Dual 350W+ CPUs and high-speed NVMe arrays), the power draw under full load significantly exceeds that of standard 1U servers.
- **Circuit Loading:** Deployments must account for **50A or 60A 208V circuits** (depending on PDU efficiency and server configuration) when racking multiple Puppet nodes. A single 2U Puppet node can easily draw 3.5kW under peak stress testing. (Data Center Power Distribution)
- **Hot-Swap Requirements:** All PSUs, cooling fans, and primary storage media must be hot-swappable to maintain N+1 redundancy during active operation. Failure to adhere to hot-swap standards will result in service interruption during routine maintenance. (Redundant Power Supply Implementation)
5.2. Thermal Management and Airflow
High component density generates significant heat flux, demanding robust cooling infrastructure.
- **Rack Density:** Limit the number of Puppet nodes per rack to maintain acceptable aisle temperatures. Standard recommendations suggest limiting the rack power density to **15kW – 20kW per rack** for traditional air-cooled facilities. Exceeding this requires liquid cooling solutions (e.g., Rear Door Heat Exchangers or Direct-to-Chip cooling). (Data Center Thermal Management)
- **Component Spacing:** Ensure adequate vertical clearance (at least one empty U space recommended between dense 2U nodes) if the rack design does not guarantee sufficient front-to-back airflow paths.
5.3. Firmware and Driver Lifecycle Management
Maintaining the complex interplay between the CPU microcode, BIOS/UEFI, chipset drivers, and high-speed NVMe firmware is paramount to achieving the advertised performance metrics and stability.
- **BIOS Updates:** Critical updates often address NUMA balancing issues, memory training stability (especially at DDR5-5200+ speeds), and PCIe lane allocation mapping. Updates must be coordinated with the hypervisor vendor's Hardware Compatibility List (HCL). (Server Firmware Management)
- **Storage Controller/Firmware:** NVMe drives require periodic firmware updates to address wear-leveling algorithms and maintain consistent IOPS performance over the lifespan of the drive. Outdated firmware can lead to sudden performance degradation or premature drive failure notifications. (SSD Endurance and Wear Leveling)
5.4. Serviceability and Component Replacement
The 2U form factor imposes physical constraints on component accessibility.
- **CPU/RAM Access:** Accessing CPUs and DIMMs usually requires removal of the entire chassis from the rack (or at least sliding it out significantly) and removal of the top cover, often requiring two technicians due to weight and cable management. (Server Field Service Procedures)
- **NVMe Replacement:** While U.2 drives are typically hot-swappable, the internal backplane cabling (especially for PCIe Gen 5.0 connections) can be delicate. Technicians must follow specific torque and latching procedures to prevent accidental dislodging of adjacent drive connections. (Direct Attached Storage Maintenance)
5.5. Operating System and Hypervisor Considerations
The operating system must be fully aware of the dual-socket architecture and the NUMA topology to correctly schedule threads and allocate memory.
- **NUMA Awareness:** Workloads must be explicitly pinned or configured to prefer local memory access (e.g., using `numactl` on Linux). Cross-socket memory access incurs significant latency penalties that negate the high-speed memory benefits. (NUMA Memory Allocation)
- **Driver Support:** Ensure the chosen OS distribution (e.g., RHEL 9, Windows Server 2022) has certified drivers for the specific PCIe Gen 5.0 Host Bridge and Network Interface Cards (NICs). Compatibility issues often manifest as dropped packets or reduced sustained throughput rather than outright failure. (Operating System Compatibility)
The Puppet configuration represents the zenith of current 2U server density, demanding high operational maturity to extract its full potential. Enterprise Servers
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️