SKU: 302026942

VRSF Exhaust Catless Downpipe For BMW 120i, 128i, 228i, 320i, 328i, 428i 2012-2017

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Description

VRSF Exhaust Catless Downpipe For BMW 120i, 128i, 228i, 320i, 328i, 428i 2012-2017Description: We are proud to introduce our VRSF Catless Down Pipe for your N20 & N26 BMW. This downpipe is THE best bang for the buck mod available. By eliminating the restrictive catalytic converter in the factory downpipe, back pressure is reduced significantly which results in faster spool, an increase in power & a more aggressive exhaust note. Our catless downpipe are hand crafted from mandrel bent SS304 grade stainless steel and the turbo flange

Description:

We are proud to introduce our VRSF Catless Down Pipe for your N20 & N26 BMW. This downpipe is THE best bang for the buck mod available. By eliminating the restrictive catalytic converter in the factory downpipe, back pressure is reduced significantly which results in faster spool, an increase in power & a more aggressive exhaust note.

Our catless downpipe are hand crafted from mandrel bent SS304 grade stainless steel and the turbo flange is precision CNC machined out of billet 304 grade stainless steel.  All VRSF products include a lifetime warranty as well as our “No Hassle” satisfaction guarantee (including fitment).

Note: We also offer ceramic coating services utilizing Cerakotes High Temperature Ceramic coating, made to withstand temperatures up to 1800F. Please note that while we do offer a lifetime warranty on the downpipes, we only offer a 1 year warranty on the ceramic coating services due to the nature of the product. We use Cerakote’s best thermal barrier coating which helps keep the hot air inside the exhaust and out of the engine bay which decreases overall intake air temps along with engine and transmission oil temps.

Features:

  • Made from hand crafted, tig welded 304 grade stainless steel.
  • Smooth radius bends to decrease turbulence and increase flow.
  • CNC SS304 Stainless Steel Flanges.
  • Gains from 20-25hp with tune.
  • VRSF Lifetime Warranty

        Vehicle Fitment Chart:

        Make Model Chassis Engine Year

        		320i & 320xi
        		 F30
        		2012-2017

        		328i & 328xi F30
        		2012-2017

        		420i & 420xi
        		 F32/F33
        		2013-2017

        		428i & 428xi
        		 F32/F33
        		2013-2017
        BMW 428i & 428xi
        Gran Coupe
        		 F36
        		2013-2017
        128i & 128xi F20/F21 2012-2017
        220i & 220xi F22 2013-2017
        228i & 228xi F22 2013-2017


        Note:

        Images are for representation purpose only and may differ depending on application

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          Exchange/Return Notes
          • We offer a 30-day return/exchange service after receiving.
          • Final sale items are not eligible for returns or exchanges.
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          SKU: 302026942

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          4.4 ★★★★★
          Based on 30 reviews
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          Product Reviews
          O
          Om S
          Massapequa, US
          ★★★★★ 4
          Title: Really Good Book for Learning LLMs
          Format: Paperback, Format: Paperback
          I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon. The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice. Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening. Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development. Recommended for anyone building AI systems professionally.
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on July 25, 2025
          J
          Jiewen Wang
          Draper, US
          ★★★★★ 5
          a comprehensive guide at the intersection of generative AI and cybersecurity
          Format: Kindle
          This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity. What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike. Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on July 2, 2025
          N
          Nader
          Pawtucket, US
          ★★★★★ 1
          Light on substance and heavy on flaws
          Format: Paperback
          The book has a great list of topics, but fails to provide much substance any of them. Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!) Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples). Seems like the book was rushed, and the author has limited hands on experience (if any). At least we know based on the amount of flaws that it was not written by an LLM
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on December 31, 2025
          N
          noam barkay
          Lowell, US
          ★★★★★ 5
          Excellent book to truly understand LLM design patterns
          Format: Paperback
          I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket! Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC. - How to prepare your data for training by making it extremely clean. Developing the brains: the practical aspects of training, optimizing, and maintaining your models. - Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities. Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise. - It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!) Really, this feels like a useful toolkit, so Ken thank you for that resource Thanks, Idan Habler
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on June 9, 2025
          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

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