Google’s latest TPU generations continue to demonstrate exceptional performance across AI workloads, with TPU v5e and v6 delivering competitive — and in some cases superior — results compared to NVIDIA’s leading accelerators.

However, new reports reveal a critical bottleneck that could hinder the platform’s ability to scale horizontally beyond internal Pod boundaries.

Impressive On-Pod Performance

Benchmarks indicate:

• High throughput for training large language models
• Strong inference performance
• Better power efficiency vs previous TPU generations
• Excellent scaling within a single Pod consisting of thousands of chips

This makes TPUs extremely powerful inside Google’s tightly integrated infrastructure.

The Overlooked Bottleneck: External Interconnect

While TPU clusters scale efficiently inside Pods, external scaling between Pods is where the system struggles.

Key limitations include:

1. External interconnect bandwidth drops significantly compared to intra-Pod links
2. Performance deteriorates when expanding across tens of thousands of chips
3. Synchronization challenges arise across distributed Pods
4. Large-scale model training suffers from communication delays

In simpler terms:

TPUs are extremely fast — until they need to talk to each other across Pods.

Why This Is a Serious Issue

Training frontier-level AI models requires:

• Tens of thousands of accelerators
• High-bandwidth, low-latency communication
• Synchronization across global data centers
• Massive parameter and gradient exchange

This is where NVIDIA dominates with NVLink, NVSwitch, and InfiniBand, enabling clusters with smoother performance at huge scale.

If Google cannot fix this bottleneck, TPU clusters may fall behind when building “beyond-trillion-parameter” models.

What’s Next for Google?

Analysts expect Google to work on:

• A new high-speed TPU interconnect
• Compiler-level optimizations
• Data compression for inter-Pod communication
• Specialized networking fabrics for hyperscale AI training

But until then, TPU’s external scaling remains a critical concern.