Description
By Carter James | Oplexa Insights
Dec 2025 | 15 min read
The AI revolution is powered by large language models (LLMs), and NVIDIA’s GB200 platform—with NVL 72 and NVL 36 GPUs—is at the forefront. LLMs, such as GPT-style models or multimodal transformers, require ultra-high-speed computation across thousands of cores.
This creates a massive opportunity for connector companies like Amphenol (APH), TE Connectivity (TEL), and Molex, whose backplane, in-tray, and optical interconnects are critical for scaling these LLM workloads. Emerging technologies such as TFLN photonics and operational insights from semiconductor IT G&A benchmarking further optimize cost and performance for LLM deployment.
Introduction to the NVIDIA GB200 System (NVL 72 & NVL 36)
Imagine training a 1T-parameter LLM in real time. Without fast GPU interconnects, throughput bottlenecks appear immediately. The GB200 system, featuring NVL 72 and NVL 36, solves this by providing:
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Massive parallelism for transformer layers
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High GPU-to-GPU bandwidth to keep LLM pipelines flowing
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Dense rack-level compute, allowing 50+ NVL 36 GPUs per rack
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Advanced thermal and power interconnects to sustain continuous LLM training
Every NVL 36 rack becomes an LLM powerhouse, but only if connectors, cabling, and optical links keep pace.
Content Opportunity for Connector Companies per Rack
LLM training scales with the number of GPUs and the speed of data transfer. In NVL 36 racks:
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Backplane connectors handle terabits of LLM tensor data between GPUs
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DAC/AOC cabling ensures low-latency connections for multi-GPU training
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Optical interconnects powered by TFLN photonics allow LLMs to scale beyond rack boundaries
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In-tray connectors sustain high throughput and reliability for continuous LLM inference
Supplier potential:
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APH: Backplane and power connectors for high-density GPU racks
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TEL: Signal integrity for high-speed LLM tensor communication
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Molex: Optical and cable assemblies for extreme LLM scaling
Each NVL 36 rack can generate significant connector content, especially as model sizes grow.
Market Outlook for Backplane Cabling and Connectors
LLM growth is accelerating faster than traditional AI workloads. Drivers include:
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Deployment of massive NVL 36 clusters for inference and fine-tuning
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Expansion of multimodal AI models requiring GPU-to-GPU bandwidth
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Adoption of TFLN photonics for low-latency optical links
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Infrastructure optimization guided by semiconductor IT G&A benchmarking
The interconnect market will continue high double-digit growth over the next decade as LLM workloads dominate data-center compute.
Average Pricing Analysis for Backplane Cabling and Connectors
Pricing reflects the critical role connectors play in LLM performance:
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Backplane connectors: Premium-priced due to high-speed and dense GPU demands
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AOC/DAC cabling: Price rises with length, speed, and thermal resistance
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Optical connectors (TFLN photonics-enabled): High investment, but necessary for massive LLM throughput
Factors influencing pricing include material cost, signal integrity, thermal performance, and integration with NVL 36 GPU trays. For hyperscalers, these are non-negotiable costs to ensure real-time LLM inference.
Competitive Landscape: APH, TEL, Molex, and Others
Competition is intense, but differentiated by LLM-enabling capability:
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APH: Strong in power and backplane connectors for high-density NVL 36 clusters
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TEL: Signal-integrity expert ensuring stable LLM data flows
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Molex: Leading optical and photonics-ready cable assemblies
Companies investing in TFLN photonics and faster interconnect R&D will capture the bulk of the LLM-driven market.
Interconnect Products for Data Center Applications
For LLM-centric AI data centers, interconnects are no longer just “supporting hardware” — they are LLM accelerators:
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High-bandwidth backplane connectors
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GPU-to-GPU and CPU-to-GPU DAC/AOC cables
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Optical modules using TFLN photonics
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Board-to-board connectors ensure low-loss paths for large transformer models
Without these, LLM inference slows, training times balloon, and AI ROI drops.
Technological Advancements in Interconnect Solutions
To meet NVL 36 LLM demands:
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Co-Packaged Optics (CPO) reduces latency and power consumption
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TFLN Photonics provides low-loss, high-speed optical transmission
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224G PAM4 signaling supports multi-GPU LLM scaling
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Low-loss dielectrics and thermal-optimized connectors sustain continuous inference workloads
These innovations are LLM-critical, not optional.
Insights on Share Distribution Between Connector Players
Market share is driven by LLM-readiness:
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APH: Dominates backplane and power
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TEL: Leading in high-speed, signal-integrity connectors
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Molex: Optical and photonics solutions
Future share shifts will favor companies that integrate TFLN photonics into NVL 36 clusters and meet LLM performance expectations.
Future Growth Opportunities for APH, TEL, and Molex
LLM growth creates clear pathways:
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Optical connectors using TFLN photonics for large-scale GPU clusters
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Thermal-optimized backplane and in-tray connectors for NVL 36 racks
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Infrastructure planning using semiconductor IT G&A benchmarking for cost-efficient scaling
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Partnerships with hyperscalers and NVIDIA for LLM-centric deployments
LLM workloads will drive massive interconnect demand for years to come.
Conclusion and Strategic Recommendations
NVL 72 and NVL 36 are LLM powerhouses. Connector companies must:
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Expand high-speed and optical product lines
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Invest in TFLN photonics R&D
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Leverage semiconductor IT G&A benchmarking to optimize cost
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Strengthen hyperscaler partnerships
This ensures a strong competitive position in the LLM-driven AI market.
Appendices
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NVL 36 rack connector content forecast
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Pricing ranges for backplane and optical interconnects
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Hyperscaler LLM deployment list and projected growth
Frequently Asked Questions
1. What is NVL 36?
NVL 36 is NVIDIA’s high-performance GPU built for large-scale LLM training and inference, offering high memory bandwidth, low-latency interconnects, and extreme parallelism.
2. How does NVL 36 accelerate LLMs?
It enables faster model training, higher throughput, and stable inference, allowing multi-trillion parameter LLMs to run efficiently across large GPU clusters.
3. What is Semiconductor IT G&A Benchmarking?
It’s a method to compare IT and administrative spending with industry benchmarks. It helps optimize infrastructure costs while deploying NVL 36-based LLM clusters.
4. How does TFLN Photonics help LLMs?
Thin-Film Lithium Niobate (TFLN) photonics allows ultra-fast, low-loss optical data transfer, crucial for scaling LLM inference and multi-GPU training pipelines.
5. Can NVL 36 work with TFLN photonics?
Yes. NVL 36 is designed for integration with TFLN photonics, ensuring high-speed optical interconnects for massive AI workloads.
6. Why are these technologies important together?
NVL 36 powers the compute, TFLN photonics ensures fast data transfer, and semiconductor IT G&A benchmarking optimizes cost—together, they create high-efficiency, LLM-ready infrastructure.
