Google’s TPU v5x (Tensor Processing Unit version 5x) has become the talk of the AI world in 2026, yaar! These specialized chips are designed specifically for machine learning workloads and are giving NVIDIA’s H100 GPUs some serious competition. Major Indian tech companies like Infosys, TCS, and Wipro are already integrating these into their AI infrastructure.
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What Makes TPU v5x Special Compared to Previous Versions
The TPU v5x packs some insane improvements over the older TPU v4. We’re talking about 2.8x better performance per watt and 1.9x better training performance. Matlab, these numbers are huge when you’re running large language models!
Each TPU v5x pod contains 4,096 chips connected through Google’s custom interconnect technology. The memory bandwidth has been boosted to 1.2TB/s per chip, which is absolutely mental for handling massive datasets.
- Peak performance: 275 teraFLOPS of bfloat16
- High bandwidth memory: 16GB HBM2e per chip
- Interconnect speed: 4.8 Tbps per chip
- Power efficiency: 2.8x better than TPU v4
Pricing and Availability Through Google Cloud India
Here’s where it gets interesting for Indian businesses, bhai. Google Cloud India has made TPU v5x available through their Mumbai and Delhi regions since January 2026. The pricing is quite competitive compared to equivalent GPU instances.
A single TPU v5x costs around ₹45 per hour on Google Cloud Platform. For comparison, NVIDIA H100 instances are running at about ₹52 per hour. Indian startups like Ola Electric and Byju’s have already migrated some of their AI workloads to TPU v5x to save costs.
The minimum commitment is usually 1 year, but Google is offering flexible pricing for Indian educational institutions and research organizations. IIT Bombay and IISc Bangalore have secured special academic pricing at around ₹28 per hour.
Real Performance Numbers from Indian Companies
Arre, the real test is how these perform in actual production, right? Flipkart’s recommendation engine team reported 40% faster training times when they switched from TPU v4 to v5x in February 2026.
Zomato’s ML team found that their food delivery prediction models now train 65% faster using TPU v5x pods. Their monthly compute bill dropped by ₹12 lakh after the migration, which is substantial savings yaar!
PhonePe has been using TPU v5x for fraud detection since March 2026, processing over 2.3 billion transactions monthly. Their latency improved from 120ms to 78ms per prediction.
Should Indian Companies Switch to TPU v5x?
Honestly yaar, it depends on your specific use case. If you’re heavily into TensorFlow and JAX frameworks, TPU v5x is a no-brainer. The performance gains are real, and the cost savings add up quickly.
However, if your team is more comfortable with CUDA and PyTorch, the migration effort might not be worth it immediately. Companies like Paytm are taking a hybrid approach – using TPU v5x for training and GPUs for inference.
Mujhe lagta hai that by end of 2026, we’ll see more Indian AI companies adopting TPU v5x, especially for large-scale training workloads. The economics just make too much sense to ignore!