Choosing the Right Capacitors for AI Servers: Technical Guidelines for High-Performance Computing

Capacitors may seem like passive, ancillary components in server designs, yet in AI servers they play a pivotal role in enabling stability, transient response, and long-term reliability under extreme electrical stress. Selecting an inappropriate capacitor can lead to voltage droop, thermal failure, reliability degradation, or even system-level crashes. A review of key capacitors offered by the electronics division of Ymin (Shanghai Yongming Electronic Co., Ltd), a capacitor brand with in-house R&D capabilities, can help illustrate how to match the right component to the application. In this blog, we provide an engineering‑oriented, systematic guide for capacitor selection specifically tailored to AI server ecosystems.

The Role of Capacitors in AI Server Power & Signal Chains

In AI servers, the workload dynamics differ from general-purpose computing: accelerators (e.g. GPUs, TPUs, ASICs) switch power states rapidly, memory modules demand tight voltage regulation across broad frequency ranges, and peripheral systems (storage, networking) may experience high-current transients as I/O loads shift. Capacitors in these systems serve several fundamental roles:

1. Bulk energy storage / smoothing
2. Decoupling / bypass across frequency bands
3. Transient response
4. Power-loss protection

Table 1: A breakdown of capacitor characteristics for use in AI servers. (Image source: Ymin)

Table 1 shows a breakdown of the most critical specifications, and how they map to AI server requirements:

Capacitor Selection by Subsystem in AI Servers

Motherboard & VRM Stages:

• Challenges: Fast transient currents, tight voltage regulation, switching noise
• Best Capacitor Types: Multilayer Polymer Solid Aluminum, Conductive‑Polymer Tantalum, Standard Solid Polymer
• Design Guidelines: Mix bulk and HF decoupling (polymer + MLCC), minimize inductance, apply derating

Power Supply (AC/DC, DC/DC Converters):

• Challenges: High ripple, conversion inefficiencies, long runtime demands
• Best Capacitor Types: Wet Electrolytic (input), Polymer Hybrids (output), Polymer or Multilayer (HF filtering)
• Design Guidelines: Large input bulk caps, low ESR outputs, control thermal conditions

Storage / SSD / Power-Loss Buffering:

• Challenges: Must supply stored energy during outages
• Best Capacitor Types: Wet Electrolytic, Polymer Hybrid, Multilayer Polymer Solids
• Design Guidelines: Calculate E=½CV², ensure redundancy, manage leakage and aging

Networking / Interconnect / Switches:

• Challenges: Bursty traffic, EMI, dynamic load
• Best Capacitor Types: Low-ESR Polymer Solids, Multilayer Polymer Solids
• Design Guidelines: Use high-ripple rated capacitors, minimize parasitics, combine with MLCCs

Gateway, Aggregation Nodes, External Interfaces:

• Challenges: Bridge systems require robust noise suppression.
• Best Capacitor Types: Multilayer Polymer Solid Aluminum, Polymer/Hybrid Types
• Design Guidelines: Broad-band decoupling, ripple suppression, thermal derating

Multilayer polymer solid aluminum electrolytic capacitors, such as the MPD121M1ED28040R (Figure 1), are well-suited for use in each of the above server subsystems.

Figure 1: The MPD121M1ED28040R is well-suited for server subsystem applications. (Image source: Ymin)

Practical Steps & Checklist for Engineers

1. Define electrical requirements (transient, ripple, energy storage)
2. Map requirements to ESR, ripple, capacitance, lifetime, ESL
3. Select candidate capacitor families and check derating curves
4. Simulate transient performance and ripple suppression
5. Optimize PCB layout for minimal parasitic impedance
6. Verify thermal and reliability margins
7. Prototype and validate under stress tests
8. Plan redundancy and protective measures

Conclusion

In AI servers, capacitor selection must be holistic — matching electrical behavior, frequency response, thermal reliability, and aging. There is no single capacitor suitable for all roles; instead, engineers should adopt a hybrid mix to cover wideband needs. Prioritize low ESR, ripple capability, and temperature resilience. Always model, test, and validate with safety margins to ensure long-term system stability.