# # #
By Sean Park, Founder, President and CEO at Point2 Technology
GPU and XPU compute density is increasing dramatically, and interconnect bandwidth is rising faster than traditional data center wiring can support. As AI systems pack more devices into each rack and push link speeds into multi‑terabit territory, the physical infrastructure that connects these components is becoming a critical bottleneck. The copper cables that once served short‑reach interconnects reliably are now hitting fundamental limits in reach, thickness, system power consumption, and routability. These are constraints that directly cap how far rack‑level compute can scale.
Copper twin‑ax has been the default for short‑reach connectivity because it is inexpensive, predictable, and operationally simple. But copper’s physics does not scale with AI’s bandwidth trajectory. As signaling rates push past 200G/lane and into terabit‑class aggregates, copper links must become shorter, thicker, heavier, and more power‑hungry on the system level to maintain acceptable BER and signal integrity.
The NVIDIA NVL72 illustrates this cliff with uncomfortable clarity:
As hyperscalers and neo-cloud service providers attempt to expand compute density, adding more GPUs per rack, copper becomes the limiting factor. You cannot increase GPU count if you cannot physically route or install the copper required to connect them.
This is why Point2 Technology (Point2) focuses on the short‑reach domain (5–10 meters). Inside a rack or between adjacent racks, copper is not viable at the bandwidths and densities demanded by future AI clusters.
Optical fiber solves distance and bandwidth but introduces scaling behavior that becomes problematic in dense AI systems.
At modest scale, fiber reliability issues, including link flap, contamination sensitivity, and connector alignment tolerances, are manageable. But as interconnect density increases, especially in AI clusters where scale‑up (more GPUs per rack) is 10× those of scale‑out use cases, optical reliability issues grow exponentially, not linearly.
Engineering teams see:
Any of these interruptions is cascaded downstream and causes delays for multi-million-dollar training runs. Additionally, optical interconnects can represent ~10% of total compute cost in large AI clusters. Fiber is excellent for long‑reach, but for short‑reach GPU‑to‑GPU traffic, its cost, fragility, and scaling behavior are mismatched to the environment.
Point2’s e‑Tube technology introduces a fundamentally different short‑reach interconnect: RF over polymer waveguide. Instead of electrons through copper or photons through glass, e‑Tube transmits modulated millimeter‑wave signals through a thin, flexible polymer tube.
Each e‑Tube cable contains eight waveguide cores and delivers:
Critically, e‑Tube preserves the operational characteristics engineers value in copper:
This combination (scalable bandwidth with copper‑class behavior) makes e‑Tube the strongest candidate to replace copper in the short‑reach domain.
AI racks are becoming thermally and physically constrained. Liquid cooling is now standard, and copper’s bulk and routing complexity intensify those constraints. Short‑reach interconnects determine:
If GPUs can be distributed more flexibly, connected by longer, lighter, more efficient RF waveguide cables, hyperscalers can:
Short‑reach is where the copper cliff hits first, and where Point2’s e‑Tube provides immediate relief.
e-Tube is extending copper experience at different stages, each unlocking new architectural freedom.
Today’s best‑in‑class systems, such as the NVL72, top out at 72 GPUs per rack, which is a limit imposed not by compute, but by copper. As e‑Tube progresses from pluggable ARC to near‑packaged and co‑packaged designs, it unlocks the ability to scale GPU count beyond 72, enabling larger, more flexible, and more power‑efficient rack‑scale AI systems.
The copper cliff is not theoretical. It is already here in modern AI systems. Fiber solves some problems but introduces others, especially as interconnect density scales. Point2’s e-Tube RF‑over‑waveguide approach offers a balanced, engineering‑sound path forward: scalable performance, copper‑class reliability, and copper‑class economics in the short‑reach domain where AI systems are most constrained.
For data center architects and hardware engineers designing next‑generation AI clusters, the message is clear: Short‑reach interconnects must evolve, and e‑Tube is the technology that unlocks the next wave of GPU expansion.
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Sean Park is a seasoned executive with over 25 years of experience in the semiconductors, wireless, and networking market. Throughout his career, Sean has held several leadership positions at prominent technology companies, including IDT, TeraSquare, and Marvell Semiconductor. As the CEO, CTO, and Founder at Point2 Technology, Sean was responsible for leading the company’s strategic direction and overseeing its day-to-day operations. He also served as a Director at Marvell, where he provided invaluable guidance and expertise to help the company achieve its goals. He holds a Ph.D. in Electrical Engineering from the University of Washington and also attended Seoul National University.
The post The Copper Cliff: Why AI Data Centers Need a New Kind of Cable appeared first on Data Center POST.
TL;DR Rapid GPU compute density and terabit-bandwidth demands are pushing traditional short-reach copper cables to physical limits in reach, routing thickness, power consumption, and thermal management. While optical fiber addresses reach, using it for dense, short-reach GPU-to-GPU connections introduces exponential reliability issues while consuming roughly 10% of total compute cost. Point2 Technology offers an e-Tube
The post The Copper Cliff: Why AI Data Centers Need a New Kind of Cable appeared first on Data Center POST. Read More Data Center POST
# # #
By Sean Park, Founder, President and CEO at Point2 Technology
GPU and XPU compute density is increasing dramatically, and interconnect bandwidth is rising faster than traditional data center wiring can support. As AI systems pack more devices into each rack and push link speeds into multi‑terabit territory, the physical infrastructure that connects these components is becoming a critical bottleneck. The copper cables that once served short‑reach interconnects reliably are now hitting fundamental limits in reach, thickness, system power consumption, and routability. These are constraints that directly cap how far rack‑level compute can scale.
Copper twin‑ax has been the default for short‑reach connectivity because it is inexpensive, predictable, and operationally simple. But copper’s physics does not scale with AI’s bandwidth trajectory. As signaling rates push past 200G/lane and into terabit‑class aggregates, copper links must become shorter, thicker, heavier, and more power‑hungry on the system level to maintain acceptable BER and signal integrity.
The NVIDIA NVL72 illustrates this cliff with uncomfortable clarity:
As hyperscalers and neo-cloud service providers attempt to expand compute density, adding more GPUs per rack, copper becomes the limiting factor. You cannot increase GPU count if you cannot physically route or install the copper required to connect them.
This is why Point2 Technology (Point2) focuses on the short‑reach domain (5–10 meters). Inside a rack or between adjacent racks, copper is not viable at the bandwidths and densities demanded by future AI clusters.
Optical fiber solves distance and bandwidth but introduces scaling behavior that becomes problematic in dense AI systems.
At modest scale, fiber reliability issues, including link flap, contamination sensitivity, and connector alignment tolerances, are manageable. But as interconnect density increases, especially in AI clusters where scale‑up (more GPUs per rack) is 10× those of scale‑out use cases, optical reliability issues grow exponentially, not linearly.
Engineering teams see:
Any of these interruptions is cascaded downstream and causes delays for multi-million-dollar training runs. Additionally, optical interconnects can represent ~10% of total compute cost in large AI clusters. Fiber is excellent for long‑reach, but for short‑reach GPU‑to‑GPU traffic, its cost, fragility, and scaling behavior are mismatched to the environment.
Point2’s e‑Tube technology introduces a fundamentally different short‑reach interconnect: RF over polymer waveguide. Instead of electrons through copper or photons through glass, e‑Tube transmits modulated millimeter‑wave signals through a thin, flexible polymer tube.
Each e‑Tube cable contains eight waveguide cores and delivers:
Critically, e‑Tube preserves the operational characteristics engineers value in copper:
This combination (scalable bandwidth with copper‑class behavior) makes e‑Tube the strongest candidate to replace copper in the short‑reach domain.
AI racks are becoming thermally and physically constrained. Liquid cooling is now standard, and copper’s bulk and routing complexity intensify those constraints. Short‑reach interconnects determine:
If GPUs can be distributed more flexibly, connected by longer, lighter, more efficient RF waveguide cables, hyperscalers can:
Short‑reach is where the copper cliff hits first, and where Point2’s e‑Tube provides immediate relief.
e-Tube is extending copper experience at different stages, each unlocking new architectural freedom.
Today’s best‑in‑class systems, such as the NVL72, top out at 72 GPUs per rack, which is a limit imposed not by compute, but by copper. As e‑Tube progresses from pluggable ARC to near‑packaged and co‑packaged designs, it unlocks the ability to scale GPU count beyond 72, enabling larger, more flexible, and more power‑efficient rack‑scale AI systems.
The copper cliff is not theoretical. It is already here in modern AI systems. Fiber solves some problems but introduces others, especially as interconnect density scales. Point2’s e-Tube RF‑over‑waveguide approach offers a balanced, engineering‑sound path forward: scalable performance, copper‑class reliability, and copper‑class economics in the short‑reach domain where AI systems are most constrained.
For data center architects and hardware engineers designing next‑generation AI clusters, the message is clear: Short‑reach interconnects must evolve, and e‑Tube is the technology that unlocks the next wave of GPU expansion.
# # #
Sean Park is a seasoned executive with over 25 years of experience in the semiconductors, wireless, and networking market. Throughout his career, Sean has held several leadership positions at prominent technology companies, including IDT, TeraSquare, and Marvell Semiconductor. As the CEO, CTO, and Founder at Point2 Technology, Sean was responsible for leading the company’s strategic direction and overseeing its day-to-day operations. He also served as a Director at Marvell, where he provided invaluable guidance and expertise to help the company achieve its goals. He holds a Ph.D. in Electrical Engineering from the University of Washington and also attended Seoul National University.