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Technical2026-08-09 · 7 min read

FeNi vs MPP Powder Cores: Comparison & Selection Guide

Quick AnswerFeNi (50%Fe-50%Ni) and MPP (81%Ni-17%Fe-2%Mo) are both distributed-air-gap powder cores. MPP offers the lowest core loss and very stable permeability at low flux density, while FeNi provides a higher saturation flux density (Bs ≥1.5T vs ~0.75T) and stronger DC bias stability at high current, at a lower alloy cost. Choose MPP for loss-critical moderate-current filtering; choose FeNi for AI GPU VRM, server POL, EV charger and solar high-current power delivery.

FeNi and MPP are the two premium powder core families used when iron powder loss is too high and ferrite saturation is too low. Both are pressed from insulated alloy particles with a distributed air gap, but their compositions give them different strengths. This guide compares FeNi vs MPP on the properties that decide high-current inductor performance.

1. Composition and permeability

FeNi powder cores use a 50% iron – 50% nickel alloy, typically pressed to permeabilities of 14–160µ. MPP (Molybdenum Permalloy Powder) uses 81% nickel, 17% iron and 2% molybdenum, with permeabilities from 14µ up to 550µ. Higher nickel content gives MPP lower hysteresis loss but also a much higher raw-material cost.

2. Saturation flux density (Bs)

This is the biggest practical difference. FeNi cores reach Bs ≥1.5T, while MPP saturates around 0.75T. In a 60–100A VRM phase or a high-current EV charger stage, the FeNi core carries the same current in significantly less volume because the operating flux stays further from saturation. That is why FeNi dominates AI GPU VRM and server POL power delivery.

3. Core loss

MPP has the lowest core loss of the common powder cores, especially at low flux density and mid frequencies, which is why it remains the reference for precision filter inductors and output chokes. FeNi keeps core loss ≤300kW/m³ at 100kHz and stays efficient into the MHz range, with the gap narrowing at higher flux density where MPP approaches saturation.

4. DC bias stability

Both families have a distributed air gap, so inductance rolls off gradually under DC bias instead of collapsing. FeNi maintains permeability variation ≤8% across −40°C to +180°C, keeping inductance predictable when load current swings hard. MPP is also stable, but its lower Bs forces a larger core when the same peak current must be supported.

5. Temperature behavior

FeNi operates reliably from −40°C to +180°C with flat temperature dependence, suiting automotive and outdoor energy equipment. MPP has a high Curie temperature and low loss drift as well, but the practical thermal advantage in high-current designs usually goes to FeNi because the smaller core runs cooler at the same current.

6. Cost

Nickel is the dominant cost driver. MPP’s 81% nickel content makes it the most expensive common powder core; FeNi at 50% nickel is cheaper per kilogram while still offering Bs ≥1.5T. For the same inductance at high current, FeNi also needs less core material, widening the cost gap.

FeNi vs MPP at a glance

PropertyFeNi (50Fe/50Ni)MPP (81Ni/17Fe/2Mo)
Permeability14–160µ14–550µ
Saturation Bs≥ 1.5 T≈ 0.75 T
Core lossLow (≤300kW/m³ @100kHz)Lowest of powder cores
DC bias stabilityExcellent (≤8% permeability drift)Excellent
Temperature range−40°C ~ +180°CWide (high Curie temp)
Relative costModerate (50% Ni)Highest (81% Ni)
Typical roleHigh-current power deliveryLoss-critical filtering

Which one should you choose?

  • AI GPU VRM, server POL, EV charger, solar inverter → FeNi (Bs ≥1.5T, smaller core)
  • Output chokes / filters where absolute lowest loss at moderate current matters → MPP
  • Wide temperature automotive or outdoor designs → FeNi (−40°C to +180°C)
  • Cost-sensitive high-current stages → FeNi over MPP (less nickel, less material)

YUTE Magnetics manufactures FeNi powder core inductors (YTFN series) and custom high-current magnetics with IATF 16949 and AEC-Q200 qualification. Send your frequency, current and loss targets for a free engineering evaluation.

See the High Flux Cores Guide, the Core Selection Guide and the DC Bias Considerations for AI GPU VRM for deeper design guidance.

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