Introducing a truly collaborative effort exemplifying the best in the performance aftermarket industry, 100% designed and manufactured in the USA.

CSF’s new McLaren 720S Coupe/765LT Coupe High-Performance Intercooler System – the new advantage for the absolute best quarter-mile traps and half-mile speeds, designed for the fastest and most powerful McLarens in the world. Initially designed by CSF R&D partner RK Autowerks using state-of-the art 3D scanning and prototyping equipment, CSF incorporated a massive custom-spec CSF bar/plate air-to-water core that is 68% larger than the OEM unit. Voodoo Autosport was then consulted to further enhance the design for a complete drop-in fit installation that is truly plug-and-play. Lastly, but not least, CSF reached out to the best tuners of the world’s fastest McLarens, M Engineering, to performance test the coolers. Final results: 99% efficiency (vs. OEM 94%) along with lower pressure drop across the cooling system.

Custom-Spec High-Performance CSF Bar/Plate Core Built In-House

68% Increase in Core Volume vs. OEM

Single-Core Dual-Pass Design vs. Inefficient Multiple Core Design Found in Competitors Lower Quality/Less Efficient Offerings

Efficiency Tested at 99% by M Engineering vs. OEM 94%

Pressure Tested to 4bar (58psi)

CNC Machined 6061 Aluminum Billet End Tanks

Finished in CSF’s Aerospace-Grade Thermal Dispersion Space Grey Coating for Maximum Heat Transfer Efficiency and OEM+ Aesthetic

Integrated Dual 1/8-NPT Methanol Injection Bungs Positioned on Outlet Tank for Post-Intercooling Reduction of IATs

Come Pre-Plugged For Vehicles Without Methanol Injection

Can Also Be Used As Sensor Ports

Complete Drop-in Fit Package With No Modifications Required for Installation

Can Be Installed From the Rear Wheel Well Without Removing the Downpipes

Includes Laser-Cut Machined Brackets and Reinforced Silicone Couplers for Throttle Body Connections

Made in the USA

Fits:

2018+ McLaren 720S Coupe (Does Not Fit Spyder Models)

2021+ McLaren 765LT Coupe (Does Not Fit Spyder Models)