SKU: 16782401717

FASS TSD07290G Titanium Signature Series Diesel Fuel System 290GPH Dodge Cummins 5.9L and 6.7L 2005-2018 and 2021

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Description

FASS TSD07290G Titanium Signature Series Diesel Fuel System 290GPH Dodge Cummins 5.9L and 6.7L 2005-2018 and 2021FASS Diesel Fuel Systems 2005 2018 and 2021 Dodge Ram Cummins Titanium Signature Series 290GPH (TSD07290G) Part No: TSD07290G The Titanium Signature Series diesel fuel System was engineered by FASS Diesel Fuel Systems to be the top performing, longest lasting and most reliable aftermarket filtration System in the diesel industry. Rated at 290 gallons per hour you will find this System is designed for a high performance vehicle that has a large fuel

FASS Diesel Fuel Systems 2005-2018 and 2021 Dodge Ram Cummins Titanium Signature Series 290GPH (TSD07290G) Part No: TSD07290G

The Titanium Signature Series diesel fuel System was engineered by FASS Diesel Fuel Systems to be the top performing, longest lasting and most reliable aftermarket filtration System in the diesel industry. Rated at 290 gallons per hour you will find this System is designed for a high-performance vehicle that has a large fuel demands and still comes backed by a limited lifetime warranty.

This Titanium Signature Series fuel System can support your Dodge Cummins 5.9L and 6.7L diesel engine with enough fuel for 1200-1500 HP large and multiple aggressive fueling upgrades commonly used during competitions.

A Titanium Signature Series fuel System also offers advanced filtration benefits that will help protect your injection Systems, effectively extending the life of both your Bosch Injection System and Bosch Diesel Injectors. These Benefits include an improved filtration rating of 2 Microns, a built-in water separation filter and FASS Diesel Fuel Systems patented air/vapor removal process.

Aftermarket Systems are important for both daily drivers and high-performance applications when looking to improve either fuel economy or increase horsepower/torque. This kit includes two FASS BHF-1005 (Bulkhead and Suction Tube Kit). Machined from 6061 T-6 Billet Aluminum, the Bulkhead fittings function as both a suction and return tube.

This new and improved version from our previous Suction Tube Kit allows the user to plum their FASS suction and return line directly to their bulkhead fitting which are mounted directly on the fuel tank module, eliminating all cutting of the filler neck, and significantly speeds up the installation process.

The pickup tube is fabricated of PTFE Teflon which can be easily cut to adapt to different depth of fuel tanks. Properly installed, you can safely operate down to below 1/8 tank of fuel.

  • FASS Whisper Technology (FWT) was engineered to meet the stringent demands of today's quiet diesel engines. FASS Whisper Technology was in development and testing for over two and a half years to meet FASS Fuel System's high quality and durability standards. FASS Whisper Technology effectively and drastically reduces the sound levels of the Signature Series FASS Diesel Fuel System to a whisper while at the same time increasing fuel flow rates. Our competition reduces noise levels by reducing flow rates.
  • Performance Radius Cuts (PRC) are an advanced machining technique used to improve the flow of fuel through the FASS System by creating smooth flowing cuts within the aluminum housing. This prevents the reintroduction of air and vapor back into the fuel and to prevent eddy currents from forming within the corner of a 90-degree sharp angle. FASS Titanium Signature Fuel Systems are the only Fuel Air Separation System that incorporates these exclusive Performance Radius Cuts, lesser competitors commonly use T-Bone and square 90-degree fittings that can limit fuel flow, cause restrictions and reintroduce air back into your fuel.
  • Mass Flow Return (MFR) technology is used to continually polish your fuel by keeping a positive pressure on the fuel being delivered to the engine to prevent cavitation along with galling and scoring. Then returning the fuel, that's not yet needed, back to the tank after it has been passed through the filtration process, removing dirt, air, vapor and water. This means your fuel is being constantly Polished (cleaned), guaranteeing that your diesel truck always has the cleanest fuel possible. The MFR in combination with the optional FASS Fuel Heater options will help insure Extreme cold weather operations!
  • Bolt On Bracket Mounts Old mounting styles required drilling or welding when mounting your new System. This has now been simplified with our new bolt on brackets that utilize your Ford Super Duty factory bed bolt, the bolt that secures your truck bed to your frame. Mounting your Titanium Signature Series System takes only minutes and all brackets are made from machined steel for superior support and strength.

2005-2018 Cummins 290GPH System Features:

  • New Whisper Technology
  • New Bolt On Bracket Installation
  • Preset 16-18 PSI Fuel Pressure
  • 290 Gallons Per Hour Flow Rating
  • Removes Water, Air and Debris From Diesel Fuel
  • 2 Micron Filtration Rating
  • Protects and Extends The Life Of Your Injectors
  • Improved Cold Weather Starts
  • Decreases Carbon Monoxide and Carbon Dioxide Emissions
  • 1/2 Inch Fuel Lines and Ports
  • Bulkhead Fitting Suction Tube and Return
  • Fuel Mileage Improvement
  • Improved Fuel Volume Delivery
  • Smoother Engine Idle
  • Optional Electric Heater Ports
  • Optional Fuel Pressure Gauge Port
  • Limited Lifetime Warranty
  • 30,000 Mile Filter Life Expectancy
  • Quietest Motor On The Market
Shipping Notes
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Exchange/Return Notes
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SKU: 16782401717

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4.4 ★★★★★
Based on 28 reviews
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Product Reviews
O
Om S
Boise, 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
Phoenix, 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
Grantham, 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
Louisville, 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
Carnegie, 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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