Blustream PRO-IN2H2TCS 4-Way HDMI 2.0 & HDBaseT™ CSC Input Board
SKU: 51899208401

Blustream PRO-IN2H2TCS 4-Way HDMI 2.0 & HDBaseT™ CSC Input Board

$719.55 $799.50
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Description

Blustream PRO-IN2H2TCS 4-Way HDMI 2.0 & HDBaseT™ CSC Input BoardDescription The PRO IN2H2TCS board features 2 x HDBaseT and 2 x HDMI 2. 0 inputs utilising CSC technology allowing support of HDMI 2. 0 4K 60Hz 4: 4: 4 (18Gbps) HDCP 2. 2 signal distribution up to lengths of 100m (4K 70m). The four sources can be independently routed to any HDMI HDBaseT output board. The HDBaseT inputs include IR distribution to allow source control at each connected HDBaseT Transmitter. Each source input includes an independent fixed

Description

The PRO-IN2H2TCS board features 2 x HDBaseT™ and 2 x HDMI 2.0 inputs utilising CSC technology allowing support of HDMI 2.0 4K 60Hz 4:4:4 (18Gbps) HDCP 2.2 signal distribution up to lengths of 100m (4K 70m). The four sources can be independently routed to any HDMI / HDBaseT™ output board. The HDBaseT™ inputs include IR distribution to allow source control at each connected HDBaseT™ Transmitter. Each source input includes an independent fixed level 2ch analogue audio breakout and features quick release card module fittings allowing simple installation into the Custom Pro Matrix hub.

Key Features 

  • Features 2 x HDMI and 2 x HDBaseT™ inputs which can be independently routed to video output cards

  • HDMI and HDBaseT™ inputs support HDMI 2.0 18Gbps specification including up to 4K UHD 60Hz 4:4:4 and HDR resolutions*

  • HDBaseT™ inputs support signal distribution up to a distance of 100m @ 1080p (4K 60Hz 4:4:4 UHD @ 70m) when used with a compatible Blustream CSC Transmitter**

  • Supports all known HDMI audio formats including Dolby TrueHD, Atmos and DTS-HD Master Audio transmission

  • Each HDMI / HDBaseT™ input includes an independent fixed level 2ch analogue audio breakout allowing connection to a local third party audio solution (supports 2ch PCM)

  • HDBaseT™ inputs include IR distribution to allow source control at each connected HDBaseT™ Transmitter

  • Quick release card module fittings allowing simple population of input and output cards in Custom Pro Matrix Hub

  • HDCP 2.2 Support

    *Full 4K 60Hz 4:4:4 support is subject to input / output boards specified. For HDMI 2.0 18Gbps video distribution the PRO-IN2H2TCS must be used with a Blustream CSC based HDBaseT™ Transmitter


    **The PRO-IN2H2TCS does not support PoC (Power over Cable) to connected Blustream HDBaseT™ Transmitter units. A separate power supply unit must be specified

 

Specifications 

  • Video Input Connectors: 2 x HDMI Type A, 19-pin, female, 2 x HDBaseT™ RJ45 connector

  • Audio Output Connectors: 4 x Analogue Left/Right audio (3.5mm stereo jack)

  • IR Input Ports: 2 x 3.5mm stereo jack

  • Card Dimensions (W x H x D): 170mm x 22mm x 182mm

  • Dimensions Including Connections (W x H x D): 190mm x 22mm x 188mm

  • Shipping Weight: 0.5kg

  • Operating Temperature: 32°F to 104°F (0°C to 40°C)

  • Storage Temperature : -4°F to 140°F (-20°C to 60°C)


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
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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.
  • To process your return/exchange, please contact us at [email protected]
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SKU: 51899208401

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4.6 ★★★★★
Based on 2163 reviews
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Product Reviews
O
Om S
Louisville, 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
Grantham, 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
Los Angeles, 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
Draper, 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
Lake Worth, 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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