SKU: 24839662338

Toyota Supra (A90) / BMW M340i / M440i / Z4 M40i (G20 / G21 / G22 / G23 / G26 / G29) | Akrapovic | Downpipe w Cat (SS)

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Description

Toyota Supra (A90) / BMW M340i / M440i / Z4 M40i (G20 / G21 / G22 / G23 / G26 / G29) | Akrapovic | Downpipe w Cat (SS)This product is crafted from high quality stainless steel, with the front part of the downpipe cast in Akrapovis in house foundry. A sporty 300 cpsi catalytic converter is integrated into the downpipe, with the heat insulation made of stainless steel and a flexible tube incorporated into the middle section. Power gains with an Evolution Line (Titanium) fitted with the Akrapovi downpipe of 18. 7 kW (25. 4 hp) at 5,600 rpm and torque increases of 34. 7

This product is crafted from high-quality stainless steel, with the front part of the downpipe cast in Akrapovič’s in-house foundry. A sporty 300 cpsi catalytic converter is integrated into the downpipe, with the heat insulation made of stainless steel and a flexible tube incorporated into the middle section. Power gains with an Evolution Line (Titanium) fitted with the Akrapovič downpipe of 18.7 kW (25.4 hp) at 5,600 rpm and torque increases of 34.7 Nm at 2,400 rpm have been recorded when tested on the Akrapovič dyno against a BMW M440i with the standard stock exhaust. It delivers a pure racing sound, full of emotion and adrenalin-inducing enjoyment, and great feedback at high revs, all without unwanted drone. This product is meant for racetrack use only, and remapping the ECU is mandatory in order to prevent potential “check engine light” warning signals.

The downpipe is an optional product for the following Evolution Line (Titanium) exhaust systems:

BMW M340I (G20) without OPF/GPF: Slip-On Line (Titanium) + Evolution Link pipe (SS) - S-BM/T/17H + E-BM/SS/8

BMW M440I (G22) without OPF/GPF: Slip-On Line (Titanium) + Evolution Link pipe (SS) - S-BM/T/18H + E-BM/SS/8

THIS PRODUCT DOES NOT MEET EMISSION COMPLIANCE REQUIREMENTS FOR STREET OR HIGHWAY USE.

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SKU: 24839662338

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4.9 ★★★★★
Based on 29 reviews
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Product Reviews
R
Ryan Meyer
Charlottesville, US
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on. That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks. Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 10, 2025
V
Vineeth Sai
Lowell, US
★★★★★ 5
Great foundation read for security!
Format: Paperback
This book is a great read! It builds a strong foundation and I would highly recommend it for builders who are interetsed in building on LLMs and ensuring everything is secure. Security is super important and this book does it justice!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 27, 2025
C
Verified Purchase
CL
Fort Morgan, US
★★★★★ 5
Loved it
Format: Paperback
I’ve easily read dozens of tech books. I liked this one a lot. Sure, there were boring parts, but most of it was engaging, especially on dry subjects. I previously read “How AI Works” and found this more informative and way more enjoyable. I got through the 700 pages in about 5 weeks while also learning about probability and linear algebra from other books and online sources. I’d love to read something more advanced by the author, maybe getting into more modern applications. I feel more comfortable with the subject and feel I am now ready to conquer more advanced texts. I initially picked this up to give me some background before reading “How to Build a LLM (from scratch)”. I’ve ordered an intermediary Deep Learning with Python book as well, but wouldn’t mind a more advanced theory book to accompany these books. I’ll definitely be rereading sections of this book to further familiarize myself with topics like backpropagation. Highly recommend if you’re looking for a gentle, but broad introduction to the topic.
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Reviewed in the United States on November 14, 2025
A
Verified Purchase
Amazon Customer
Charlottesville, US
★★★★★ 5
A Good Place to Start Learning AI
Format: Paperback
Diving into the world of artificial intelligence can feel like stepping into a vast, uncharted ocean, and if you're looking for a reliable vessel to navigate these waters, this book is an excellent choice. However, I must be candid—this journey is not for the faint-hearted or those hoping to breeze through. The subject of AI, with its complex algorithms and intricate theories, is notoriously challenging. You won't find yourself flipping pages at a rapid pace, as this is not a title designed for speed-reading. Instead, it demands your full attention and a willingness to engage deeply with the material. At the heart of AI lies mathematics—a fundamental pillar that underpins the entire discipline. This book, while comprehensive, offers only a glimpse into the mathematical framework that drives artificial intelligence. But don’t be disheartened by this. Think of it as a solid foundation, a primer that will arm you with the essential concepts needed before you delve deeper into the more advanced mathematical intricacies elsewhere. When you do eventually tackle those more complex equations, you'll find yourself better equipped, with a clearer understanding of the principles at play. I should also mention that I'm no stranger to Andrew's work. Having explored some of his other writings, I can confidently say that he possesses a unique flair for communication. His ability to distill complex ideas into accessible language, without losing the essence of the subject, is truly commendable. Andrew writes with a certain finesse and sophistication that makes even the most daunting topics seem approachable. His style is not just informative, but also engaging, with a touch of elegance that sets his work apart from others in the field. In summary, while the path to mastering AI is undeniably steep, this book serves as an invaluable guide. It’s not just a starting point; it’s a beacon for those who are serious about understanding the intricacies of artificial intelligence. Be prepared to invest time and effort, and in return, you'll gain a solid foothold in a subject that is as fascinating as it is complex.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on September 2, 2024
M
Verified Purchase
MrGee
Bozeman, US
★★★★★ 5
An enjoyable, and seriously excellent, path to understanding Deep Learning...
Format: Paperback
Deep Learning is changing our world. If you want to understand more, this is a great place to start. Andrew Glassner is a talented explainer - I took his short course on Deep Learning and learned so much, but also came away impressed at how well he can make complex material so clear and engaging. And this book is jammed packed with insights, visuals, and clear explanations. The author has a playful, sometimes quirky style that shines through, which gives this tour a lot of personality as well as information. Very enjoyable reading - I felt like he captured all that was good about his course (and then some) and bottled it up in this book. There is a lot more material here than in that course, and it is well laid-out and organized so that it is easy to roam around and come back to review the pieces that matter to you. Even if you plan to go to on to be a world-class Deep Learning engineer or mathematician, you have to start by understanding the concepts. And this book does a great job of presenting all the core ideas in a way that makes them clear and memorable.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 5, 2021

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