As artificial intelligence (AI) models continue to grow, system design limitations have become the primary bottleneck. With advancements in conversational AI, computer vision, and recommender systems, AI models with hundreds of trillions of parameters are on the horizon. 

However, to sustain this growth, significant architectural innovations are essential, as current system designs are struggling to keep pace.

The Rapid Expansion of AI Workloads

The expansion of AI models has been astounding.

1. Transformer Model (2019): This was the largest natural language processing (NLP) model at the time, boasting 465 million parameters, fewer than the synapses in a honeybee’s brain.
2. Gshard MoE (Mid-2020): This model included more than a trillion parameters, roughly the same number of synapses as a mouse brain.
3. Future Projections: NVIDIA projects that by 2023, AI models could reach 100 trillion parameters, equivalent to the synapses in a macaque brain. If this trend continues, models with human-level synapse counts could soon be within reach. However, this is contingent upon the evolution of our computing infrastructure.

Overcoming Interconnect Bottlenecks

To keep up with the rapid growth of AI models, computational throughput must increase significantly. This means either adding more nodes or boosting the communication speed between nodes. However, even today’s most advanced systems face interconnect bandwidth limitations, maxing out at hundreds of gigabits per second (Gbps). 

Current Limitations: Copper-based interconnects face limitations in bandwidth, cost, power, density, weight, and configuration.

Tight Coupling Requirements: Today’s AI architectures rely heavily on GPU-HBM (high-bandwidth memory) and GPU-GPU communication, creating tight coupling requirements and increased latency due to the necessity of routing through the CPU to access DRAM.

Pivoting to Optical I/O and New Architectures

To address these challenges, a fundamental shift towards photonics, or optical I/O, is necessary. This technology uses light pulses instead of electrical signals to transmit data. 

• Higher Bandwidth: Optical I/O offers a significant boost in data transfer speed, enabling communication between components at much faster rates.
• Lower Latency: Light travels faster than electricity, reducing the time it takes for data to travel between processing units. 

With optical fiber there is practically no limitation on the amount of bandwidth that can be transferred and this will be critical to upcoming AI models.

ISPs and bandwidth providers will need to be ready to scale up their systems to cater to the increased bandwidth demands with the proliferation of AI.

This would require support for newer BNG routers with newer architectures such as CUPS which will help cater to increased bandwidth demands without high hardware costs.

Jaze ISP Manager integrates with all leading BNG providers to deliver high throughput RADIUS and DIAMETER services to cater to increased throughput and volume requirements of the future. 

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