What Are Google TPUs (Tensor Processing Units) - And What Are They Used For?
Google TPUs are custom AI accelerator chips (ASICs) built specifically to run machine learning workloads faster and more efficiently than general-purpose CPUs—and often more cost-efficiently than GPUs for certain model shapes and scaling patterns. Google offers them through Cloud TPU on Google Cloud.
Think of a TPU as Google’s “AI engine” designed around the core operations that dominate deep learning (matrix multiplications and related ops), then scaled into pods so thousands of chips can train one model together.
1) TPU vs GPU in one sentence
GPU: general-purpose parallel compute (great for many workloads, huge ecosystem)
TPU: purpose-built ML accelerator + pod scaling + compiler/runtime path (best when your workload maps cleanly to TPU execution)
2) How TPUs are packaged and scaled: “slices” and “pods”
A big TPU differentiator is that Google designs them to run as slices/pods with a dedicated inter-chip network (often called ICI – inter-chip interconnect), so they behave like one coherent training machine at very large scale.
Example (TPU v5p, from Google’s docs):
459 TFLOPs BF16 per chip
95 GB HBM2e
2,765 GB/s HBM bandwidth
1,200 GB/s bidirectional ICI per chip
Pod size up to 8,960 chips (and the topology details are documented)
3) What TPUs are used for (in real deployments)
A) Training large models (LLMs and multimodal)
TPUs are heavily used for large-scale training where you need:
high throughput
stable scaling across many chips
predictable pod architecture
Google positions Cloud TPUs as optimized for training and inference of AI models broadly.
B) Inference at scale (serving models)
TPUs are also used to serve models efficiently (tokens/sec per dollar and per watt). Google’s v5e, for example, is explicitly described as a combined training + inference (serving) product, with different optimizations depending on job type.
C) Embeddings + recommendations (SparseCore workloads)
Google’s newer TPU generations emphasize recommendation and embedding-heavy workloads too. For Trillium (6th gen), Google highlights a newer SparseCore designed for “ultra-large embeddings” common in ranking/recommendation systems.
D) “Google-scale” AI products (the internal side)
Google also frames TPUs as powering its own large AI products and models (Gemini and other AI-backed apps), which is a big reason they keep investing in new TPU generations.
4) TPU generations you’ll see in 2026 (practical view)
You’ll typically hear:
v5e: “value” / cost-efficient option for training + serving (smaller pod footprint)
v5p: “power” / highest performance option (very large pods, high bandwidth)
Trillium (6th gen / v6e): positioned as a big leap over v5e, including 4.7× peak compute per chip vs v5e, and doubled HBM capacity/bandwidth + doubled ICI bandwidth (per Google’s announcement).
(There are also industry reports about newer internal TPU work beyond Trillium, but for most builders, the Cloud TPU lineup above is the relevant “buyable” story.)
5) Software: how you actually run code on TPUs
TPUs are strongly tied to a compiler/runtime approach (commonly XLA) and supported frameworks.
Google markets Cloud TPUs as supporting PyTorch, JAX, and TensorFlow.
In practice:
If you’re JAX-first (or TensorFlow/XLA-native), TPU can be very smooth.
If you’re PyTorch-first, you’ll typically use PyTorch/XLA, and you’ll want to validate your exact model + input shapes + batching strategy.
6) When TPUs make the most sense
TPUs are usually most attractive when:
you’re already on Google Cloud (data + pipelines + deployment)
your workload is “classic transformer-ish” (or embeddings/recs) and scales cleanly
you care about performance per dollar / per watt at large scale
you can align with TPU-friendly execution constraints (batching, shapes, compilation behavior)