SKU: 73255554161

RV-T081| Custom Forged 2-Piece Corvette C7 Grand Sport Deep Dish Wheels

$2158.20 $2398.00
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Ships within 48 hours · Estimated delivery Aug 6 - Aug 11

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Description

RV-T081| Custom Forged 2-Piece Corvette C7 Grand Sport Deep Dish WheelsCustom Deep Dish Forged Wheels for 2017 Corvette C7 Grand Sport RV T081 RVRN Wheels Transform your 2017 Corvette C7 Grand Sport with a fully custom forged wheel upgrade engineered for performance, stance, and show presence. This RV T081 Forged 2 Piece setup is engineered specifically for the Grand Sport platform, delivering deep dish profile, lightweight 6061 T6 aerospace aluminum, and a head turning finish designed for both street and car show

Custom Deep Dish Forged Wheels for 2017 Corvette C7 Grand Sport | RV-T081 | RVRN Wheels

Transform your 2017 Corvette C7 Grand Sport with a fully custom forged wheel upgrade engineered for performance, stance, and show presence.

This RV-T081 Forged 2-Piece setup is engineered specifically for the Grand Sport platform, delivering deep dish profile, lightweight 6061-T6 aerospace aluminum, and a head-turning finish designed for both street and car show builds.

Whether you’re building for SCCA track days, weekend cruising, or prepping for Cars & Coffee / Corvette gatherings / local shows, this setup delivers performance you can feel and style you can flex.

Designed for: 2017-2019 Corvette C7 Grand Sport

Custom Wheel Project Details

Customer Build: 2017 Corvette C7 Grand Sport
Brake Kit: OEM
Wheel Series: RV-T081 — Deep Dish Forged 2-Piece Series

Wheel Size:
Front Wheel: 19x10
Rear Wheel: 20x12

Center Bore: 70.3mm
PCD: 5x120.65

Finish:
Spoke: Gloss Black
Barrel: Gloss Black
RVRN Red Trim & Screws

Construction: Forged 6061-T6 aerospace aluminum
Profile: Deep Dish

Engineering & Standards

Each RVRN custom forged wheel is DOT-verified and engineered for high load capacity, with each wheel tested at 4.5× the design load to ensure structural reliability.

• 6061-T6 aerospace aluminum
• Standard: 5-year Structural Warranty
• Optional: R-12K Lifetime Structural Warranty
• CNC machined to order
• Balanced for high-speed stability
• TÜV/DOT certified testing methodology

Production Process

Production Time: 4 weeks

After checkout, our engineering team will create final technical drawings based on your selected fitment.
You’ll receive 3D renderings showing concavity, brake clearance, and final finish.
Production begins only after your approval.

Fitment Guidance

If you’re unsure about offsets, brake clearance, or stance — our team will help optimize the setup for street driving, track use, or show build goals.

Call / Text: 917-807-1422
Email: [email protected]

Built for the spotlight

This wheel setup was developed with car show stance and performance balance in mind, perfect for Corvette gatherings, local shows, and weekend meetups. If you plan to feature your build, let us know — we love supporting customer cars at events.

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 73255554161

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4.1 ★★★★★
Based on 12 reviews
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Product Reviews
N
Nader
Lexington, 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
Whiting, 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
Massapequa, 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
Whiting, 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
Belleville, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 14, 2025

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