Multilayer ceramic capacitors (MLCCs) are tiny components that store and release electrical energy, steady the voltage delivered to chips, and suppress electrical noise. AI data centers use unusually large numbers of them because GPU- and accelerator-heavy servers draw much more power, change load rapidly, and leave little room around the processors for bulky components. TAIYO YUDEN estimates about 10,000–20,000 MLCCs in an AI server, compared with about 2,500 in a general server; that is a company estimate, not a universal count.
What an MLCC is
MLCC stands for multilayer ceramic capacitor. The component is built from alternating ceramic dielectric layers and metal electrodes, then packaged in a very small rectangular body. The layered construction provides useful capacitance in a compact footprint.
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A capacitor temporarily stores electrical charge and releases it when the circuit needs support. In a server, that action is mainly used for decoupling: placing capacitance close to an integrated circuit so the local power rail remains stable when the chip’s current demand changes. MLCCs can also absorb electrical noise, pass alternating-current components while blocking direct current, and be used in frequency-selective filtering.
The capacitor does not generate power and cannot replace a power supply. It works with the supply, converters, board traces and the processor’s power-delivery network. Its value is speed and proximity: a small capacitor beside an IC can respond to a fast transient before energy from a more distant source can travel through the board.
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Why AI servers need more capacitors
Accelerators create larger and faster load changes
AI servers concentrate high-power GPUs or other accelerators on a board. Training and inference workloads can change the activity of thousands of processing units in very short intervals. The power system must supply the average load and also control the brief voltage disturbances created when the load rises or falls. Local decoupling capacitance supplies or absorbs part of that transient energy.
Samsung Electro-Mechanics wrote in a 2025 article that AI servers can consume more than five to ten times the power of conventional servers and therefore require a higher number of MLCCs. That is the manufacturer’s published comparison, not a measurement that applies to every AI platform. The exact demand depends on the accelerator, memory, networking hardware, operating voltage, workload and the server’s power architecture.
Capacitance must be close to the IC
Electrical traces have resistance and inductance. As the distance between a capacitor and a processor increases, those parasitic effects make the capacitor less effective at the fastest transients. Engineers therefore distribute MLCCs around processor power pins, on power-converter outputs and at other points where a rail needs local support.
AI boards make that placement difficult. Multiple large accelerators, high-bandwidth memory, networking devices and cooling hardware compete for the area around the chips. The practical response is to obtain more capacitance from smaller packages and, where the board allows it, to embed capacitors in the circuit structure rather than consume additional surface area.
Higher total capacitance raises the component count
When a rail needs more total capacitance, designers can use larger-value parts, more parts in parallel, or a combination. Parallel MLCCs share current and lower the effective impedance over the frequencies for which they are effective. Because the highest-capacitance parts still have package, voltage and temperature limits, a design often uses a population of different values and sizes rather than one universal capacitor.
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How many MLCCs are in an AI server?
TAIYO YUDEN’s 2025 MLCC product-strategy presentation estimates approximately 10,000–20,000 MLCCs per AI server, versus approximately 2,500 per general server.
| Server category | Published estimate | How to interpret it |
|---|---|---|
| AI server | Approximately 10,000–20,000 MLCCs | TAIYO YUDEN’s estimate for an AI server; it is not an audited industry-wide average. |
| General server | Approximately 2,500 MLCCs | TAIYO YUDEN’s comparison point for a general server. |
These figures describe a server, not an entire data center. A facility’s total would depend on how many servers it operates and whether the count includes only the compute board or also storage, networking, accelerator, power and other boards. The source does not establish a single bill of materials that applies to every vendor or generation.
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There is no independent, industry-wide total in the available material. Multiplying the estimate by a data center’s server count without defining the equipment included would create a false sense of precision.
Where MLCCs sit in the data-center power path
Power enters a facility through large power systems, reaches a server’s power-supply units (PSUs), and is converted through intermediate bus converters (IBCs) and local power stages before reaching processors and memory. TDK’s July 2026 application note discusses MLCC requirements across these data-center power stages.
At the PSU and converter stages
Capacitors at a converter’s input and output help smooth switching waveforms and control ripple. The voltage rating, ripple environment, temperature and control-loop design determine which parts are suitable. These capacitors are part of the engineered power stage; replacing them with a visually similar component can change stability or reliability.
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At the processor power rail
Near a GPU or ASIC, MLCCs provide high-frequency decoupling in the smallest available area. Their effectiveness depends on placement, connection geometry and the combined impedance of the capacitor network and board. This is why a data-center board can contain dense fields of tiny capacitors around each accelerator.
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Surface-mounted MLCCs are placed on the outer layers of a circuit board. Embeddable MLCCs are designed to occupy space within the board structure, which can free surface area near an IC. TAIYO YUDEN’s AI-server material identifies both surface-mounted and embeddable approaches.
Examples of the small, high-capacitance parts being developed
Manufacturers are increasing capacitance while keeping package footprints small. The following examples illustrate that direction; they are not a universal AI-server bill of materials or interchangeable recommendations.
| Manufacturer and announcement | Capacitance | Package or dimensions | Mounting or stated use |
|---|---|---|---|
| TAIYO YUDEN, AI-server solution material | 10 μF | 0.6 × 0.3 mm | Compact MLCC example for constrained layouts |
| TAIYO YUDEN, AI-server solution material | 47 μF | 1.0 × 0.5 mm | Compact MLCC example |
| TAIYO YUDEN, AI-server solution material | 100 μF | 1.6 × 0.8 mm | Compact MLCC example |
| TAIYO YUDEN, 2025 announcement | 22 μF | 1005 size (1.0 × 0.5 mm) | Embeddable part for AI-server IC power-line decoupling; commercialization and mass production announced |
| Murata Manufacturing, 2025 announcement | 47 μF | 0402-inch size (1.0 × 0.5 mm) | Mass production announced; the company called it the world’s first mass production of this size-capacitance combination |
| Samsung Electro-Mechanics, 2025 article | 47 μF | 0402-inch size | Example of a high-capacitance part for AI-server applications |
| Samsung Electro-Mechanics, 2025 article | 100 μF | 0603-inch size | Example of a high-capacitance part for AI-server applications |
A package label alone does not establish electrical equivalence. Two parts with the same nominal capacitance and dimensions can differ in voltage rating, capacitance retention under applied bias, dielectric behavior, loss, temperature capability and reliability qualification.
How engineers select MLCCs for an AI power system
1. Start with the required capacitance at operating voltage
The printed capacitance is a nominal value. The design must consider how much capacitance remains at the rail’s actual DC voltage and across the expected temperature range. Engineers also determine how many parts are needed in parallel to meet transient and impedance targets.
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2. Check voltage rating and derating
An MLCC needs a voltage rating appropriate for the rail, with engineering margin for normal operation and transients. A part that fits physically but has inadequate voltage margin is not a safe substitute.
3. Evaluate ESR and ESL
Equivalent series resistance (ESR) and equivalent series inductance (ESL) affect how quickly and efficiently a capacitor responds. Very low ESL is especially valuable when the part must handle fast changes close to an accelerator. The useful comparison is the complete impedance curve in the intended circuit, not capacitance alone.
4. Match temperature and reliability requirements
AI accelerators generate substantial heat, so nearby components must operate over the board’s specified temperature range and lifetime. Mechanical stress, board flexing, solder-joint reliability and the manufacturer’s qualification data all matter in addition to the nominal electrical values.
5. Design the placement and interconnect
Short, wide connections and an appropriate return path reduce parasitic inductance. The number of capacitors, their distance from the IC, and the division between surface-mounted and embedded parts are layout decisions made together with the power-converter design.
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Adding capacitance can change a converter’s control-loop behavior, startup characteristics and transient response. The selected network must be validated in the actual PSU, IBC or local regulator rather than treated as a generic upgrade.
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Why smaller packages are valuable—and difficult
A smaller footprint lets designers place more capacitance in the narrow ring around a GPU or ASIC. It also leaves room for signal routing, memory and cooling structures. Achieving high capacitance in that volume requires thinner dielectric layers and more internal electrode area, which increases manufacturing difficulty and makes voltage, temperature and mechanical limits important.
High-capacitance MLCCs can also show reduced effective capacitance when voltage is applied. Consequently, a design that needs a particular amount of capacitance under load may require more nominal capacitance or more parallel parts than a simple label comparison suggests. The exact behavior is part-specific and must come from the component’s electrical data.
What the headline does not mean
- It does not mean every AI server contains exactly 10,000–20,000 MLCCs. That range is TAIYO YUDEN’s 2025 estimate.
- It does not mean AI data centers use one special capacitor type. Their boards combine parts selected for different voltages, frequencies, locations and temperatures.
- It does not mean the most capacitance in the smallest package is always best. Voltage margin, effective capacitance, impedance, reliability and converter stability can outweigh nominal value.
- It does not make a consumer capacitor assortment suitable for data-center repair. Server power networks require parts with documented specifications and validated assembly processes.
How to read manufacturer claims responsibly
The figures in this area are largely manufacturer announcements and product materials. Treat the five-to-ten-times power comparison from Samsung Electro-Mechanics, the 10,000–20,000 and 2,500 component estimates from TAIYO YUDEN, and the 2025 high-capacitance product announcements from Murata Manufacturing and TAIYO YUDEN as claims tied to those companies’ stated contexts.
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Product status and server designs can change. The announcements cited here were made in 2025, while TDK’s application note discussed in this article is dated July 2026. Before specifying a part, an engineer should consult the current datasheet, qualification information, approved manufacturer list and the server platform’s power-design requirements.
The practical takeaway for data-center design
AI servers need many MLCCs because they combine high and rapidly changing power demand with extremely dense layouts. MLCCs provide distributed, fast electrical support at the converter, board and IC levels, while filtering noise and ripple. The industry’s response is not simply to add random capacitors: it is to raise useful capacitance per unit area, place it closer to the load, and qualify the network for voltage, temperature, mechanical and power-converter conditions.
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