Vishay WSLF1206 5 W Power Metal Strip® Shunt: Higher Efficiency for AI Servers and Edge Systems

Vishay WSLF1206 5 W Power Metal Strip® Shunt Optimizes Power for AI Hardware

Compact 1206 package, 5 W power — built for AI and HPC power delivery

Vishay's new WSLF1206 Power Metal Strip® shunt resistor delivers up to 5 W in a 1206 surface-mount package. With resistance as low as 0.3 mΩ and TCR down to ±75 ppm/°C, it enables accurate current sensing with minimal power loss. Key benefits include high power density (>650 W/in²), low thermal EMF (<3 μV/°C), and ultralow inductance (<5 nH).

Why this matters for AI servers, GPUs, and edge devices

Modern AI systems prioritize power efficiency and tight voltage regulation. The WSLF1206 is ideal for VRMs, GPU power stages, data-center power distribution, and battery management in AI robots. Its low resistance reduces I²R losses, improving overall system efficiency and thermal headroom in dense racks and HPC nodes.

Designers gain space savings by avoiding parallel resistors. The device supports wide operating temperatures (-65 °C to +170 °C) and offers resistances from 0.3 mΩ to 3 mΩ with tolerances of ±1% to ±5%.

Applications and reliability

Typical uses: current sensing in switch-mode and linear PSUs, power amplifiers, inverters, UPS systems, motor drives, robotics, HVAC, and high-performance computing platforms. The WSLF1206 is RoHS-compliant, halogen-free, and sulfur-resistant for robust field performance.

Keywords: Vishay, WSLF1206, Power Metal Strip, shunt resistor, 1206 package, 5 W, low TCR, 0.3 mΩ, current sensing, power density, low inductance, VRM, AI servers, data center, GPU, battery management.

Ready for sampling and mass production with an 8–10 week lead time. Request samples from Vishay or your authorized distributor to test in your AI power designs. Optimize efficiency now and reduce thermal constraints in your next-generation AI hardware.

Call to action: Evaluate the WSLF1206 in your power stage, request samples, and update your BOM to improve efficiency for AI and HPC deployments.

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