SKU: 65216152104

AMS PERFORMANCE R35 GT-R RACE X FRONT MOUNT INTERCOOLER

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Description

AMS PERFORMANCE R35 GT-R RACE X FRONT MOUNT INTERCOOLERAMS designed the Alpha Performance R35 GT R Race X Intercooler upgrade to be the best performing 1200+HP front mount intercooler on the market. It certainly had to be in order to meet the requirements of our team of 7 second Alpha X GT Rs! If your GT R produces in excess of 1200 horsepower, especially if you drive in a hot weather climate, the Alpha GT R Race X Intercooler is your best option for producing power safely and efficiently. After testing,

AMS designed the Alpha Performance R35 GT-R Race X Intercooler upgrade to be the best-performing 1200+HP front mount intercooler on the market. It certainly had to be in order to meet the requirements of our team of 7 second “Alpha X” GT-R’s!  If your GT-R produces in excess of 1200 horsepower, especially if you drive in a hot weather climate, the Alpha GT-R Race X Intercooler is your best option for producing power safely and efficiently. After testing, re-testing, and a host of refinements, this 7 second race-proven design has been perfected and now available for your high horsepower R35 GT-R.

  • Provides 350% more volume than stock
  • Massive 6″ thick high-quality Garrett Core
  • Vertical flow and 22 fins per inch for the ultimate cooling
  • 3″ billet CNC inlet and outlet ports for improved strength and airflow
  • Durable 5-ply silicone couplers included
  • Included shrouds and bridge plates channel air into the intercooler
  • Pressure tested to 50psi
  • High-quality aluminum TIG-welded construction
  • Comprehensive Installation Instructions

MASSIVE 6″ Thick High Quality Garrett© Intercooler Core

To effectively reduce charged air temperatures, we employed a vertical flow design for ultimate cooling. With its massive 22.2″ x 14″ x 6″ bar-and-plate core the Race X offers 350% more volume than a stock pair of GT-R intercoolers. That is 1800sq inches of cooling! The core features up to twice the fin density of most intercoolers currently on the market. This allows for more contact with cool, dense air which allows for the most pavement shredding power output you can get for your GT-R! In our test vehicles making 1700+whp we saw a 20˚f difference at the end of the ¼ mile between our standard Race FMIC and our new RACE X Intercooler.

Beautiful Billet End Tanks

AMS’ Snailworks© engineering team designed the Alpha GT-R Race X billet intercooler end tanks with extremely smooth features and geometry for the best flow resulting in maximum horsepower. Designed using Solidworks© 3D CAD software, these end tanks have improved flow over boxy sheet metal end tanks found on our competitor’s inferior intercoolers. These large end tanks feature 3-inch CNC billet inlet and outlet ports to provide a smooth, tapered transition from intake to the intercooler. Our team went even further by computer designing precisely molded, custom silicone couplers to provide perfect fitment and optimized geometry for improved airflow volume and velocity – providing unmatched power without sacrificing response.

The Difference is in the Details

This intercooler kit takes full advantage of the GT-R’s available frontal area while improving functionality of the stock intercooler shroud. For even better channeling of air, a supplied bridge plate and shroud plates create a tight seal surrounding the core. This seal ensures that cool air entering the shroud is funneled through the core. The plates stop air from escaping through the sides, above and below the intercooler maximizing the efficiency and effectiveness of the intercooler core. These are features often overlooked by the competition. This intercooler does require cutting of the factory crash beam and riveting or welding in the supplied beam plate.

Engineer. Build. Perform.

Just like all Alpha GT-R components, there is absolutely no doubt about durability, reliability, and fitment of this intercooler. Our Alpha GT-R Race X Intercooler mounts firmly to the core support with gusseted aluminum brackets. All 5-ply silicone couplers provide superior strength and fasten securely using Murray Turbo Seal clamps. The intercooler itself is pressure tested to 50psi and checked for leaks in order to pass inspection. All of this is done to ensure your Alpha GT-R Race X Intercooler lasts the lifetime of your high horsepower GT-R!

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SKU: 65216152104

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4.6 ★★★★★
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Par
Lake Worth, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
Los Angeles, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Amazon Customer
Belleville, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
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Kindle Customer
Waukegan, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
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Tommy Jonsson
Massapequa, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026

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