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Description
1050 CFM Holley Dominator SP Carburetor - 0-80688Overview: Holley engineers have taken the state of the art Gen 3 Ultra carburetors and created a budget friendly version that is perfect for street strip applications as well as all out bracket or sportsman drag cars. The new Holley SP Dominator carburetors feature the patent pending Gen 3 main body which is 5 16" taller than the previous generation! This new height allowed for larger radius air entries to deliver smoother flow that equates to
Overview:
Holley engineers have taken the state-of-the-art Gen 3 Ultra carburetors and created a budget friendly version that is perfect for street/strip applications as well as all out bracket or sportsman drag cars. The new Holley SP Dominator carburetors feature the patent pending Gen 3 main-body which is 5/16" taller than the previous generation! This new height allowed for larger radius air entries to deliver smoother flow that equates to improved throttle response and consistency. An extended air cleaner flange is incorporated for easy air scoop attachment when using the available air scoop adapter. Other features include all aluminum construction, tunable billet metering blocks, throttle position sensor mounting points and knurled (hand adjustable) curb idle screws. Each calibration has been dyno and track tested to deliver proven performance!
Features:
- All aluminum construction saves 3.8lbs. over original models (28% weight savings)
- Shiny with billet aluminum metering block and black hardware
- Designed for use on Drag race, Pro Street, or any high horsepower engines
- Calibrations optimized for today's race engines and new fuels
- Fully machined from venturi throat to throttle bore for predictable & repeatable performance
- Annular boosters deliver excellent signal and throttle response
- Screw in air bleeds offer precise tuning capabilities
- Metering blocks feature improved durability, true gasket sealing and adjustability ( power valve channel restrictors, intermediate restrictors & idle feed restrictors)
- Dual 50cc accelerator pumps provide the perfect amount of fuel from idle to full throttle
- 4 corner idle allows you to precisely control your idle system
- Clear sight windows on fuel inlet side of bowls for easy and safe fuel level adjustments
- Notched float and jet extension are factory installed
- 5/8-18 inlet threads offer a large variety of plumbing options (uses standard 4500 fuel inlet spacing)
- Factory ready TPS mounting points for use with data acquisition systems (Holley TPS P/N 534-214 available)
- Knurled (hand adjustable) primary and secondary curb idle screws allow for easy idle adjustment without the use of tools
- Contoured hex head squirter screws for streamlined airflow
- Progressive secondary link installed for perfect off the line launches. No linkage parts under the carburetor (1:1 wire link included in kit)
- 100% wet-flow tested by Holley technicians to assure it arrives ready to run!
- Compatible with the Gen 3 Dominator 4-hole billet spacer P/N 17-86
Application:
| Year | Make | Model | Submodel | Engine Size |
|---|---|---|---|---|
| Universal |
Specs:
| Barrels | 4 |
|---|---|
| Billet Color | Clear SP Dominator |
| Brand | Holley |
| Carburetor Use | Professional Race |
| CFM | 1050 |
| Choke | None |
| Circuit | 2 |
| Condition | New |
| Emission Code | 4 |
| Finish | Shiny |
| Fuel | Gasoline |
| Fuel Inlet | Dual |
| High Speed Air Bleed | 32 |
| Idle Air Bleed Size | 57 |
| Marine Use | No |
| Material | Aluminum |
| Model | 4500 |
| Needle and Seat Size | 120;120 |
| Primary Main Jet | 84 |
| Primary Power Valve | 5.5 |
| Primary Pump Nozzle Size | 35 |
| Product Type | Carburetor |
| Secondaries | Mechanical |
| Secondary Main Jet | 94 |
| Secondary Pump Nozzle Size | 35 |
| Supercharged Application | No |
| Throttle Bore | 2.000 inch |
| Venturii Size | 1.690 inch |
| Warning | California Proposition 65 |
| Warranty | Limited 90 Day |
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4.3 ★★★★★
Based on 313 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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
★★★★★ 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
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Reviewed in the United States on December 31, 2025
★★★★★ 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
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Reviewed in the United States on June 9, 2025
★★★★★ 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.
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Reviewed in the United States on August 10, 2025