Bringing Google TPUs to Oracle Cloud Infrastructure

As AI workloads scale across every industry, organizations need compute options that optimize throughput, latency, and unit economics across the AI lifecycle. To meet this demand, Oracle and Google Cloud are partnering to bring Google Tensor Processing Units (TPUs) to Oracle Cloud Infrastructure (OCI), making OCI the first hyperscaler outside Google to offer TPUs. Together, we will deploy TPU capacity on OCI starting in the calendar year 2027.

TPUs: Co-designed from silicon to software

Google built its first TPU more than ten years ago on a clear product thesis: co-designing custom silicon alongside optical networking, power delivery, liquid cooling, and compiler software yields gains in performance per watt and total cost of ownership that isolated components cannot match.

TPU v1 delivered 30x the throughput per watt of contemporary processors. Today, TPUs train and serve Gemini across Google’s billion-user products, and the eighth-generation TPUs deliver up to 12.6 PFLOPS of native FP4 compute per chip, over 100x TPU v1’s 92 TFLOPS at up to 2x the performance per watt of the previous generation. However, raw FLOPS alone do not determine customer economics across training and inference. For training, the governing metric is goodput; the percentage of time a cluster spends doing productive computation. For inference, the governing metrics are latency and cost per token. TPUs address both through system-level co-design.

By pairing on-die Inter-Chip Interconnect (ICI) routing with Optical Circuit Switching (OCS), the control plane detects hardware or link degradations and dynamically routes optical paths around faults in real time without job restarts, sustaining over 97% goodput across thousands of chips  on large-scale training runs and high availability for production serving. Increased on-chip SRAM, more HBM capacity, and a new low-diameter interconnect topology that cuts network hops by more than 50% allow the latest generation TPUs to deliver up to 80% better performance per dollar for low-latency serving. 

This hardware efficiency translates into product velocity when developers can adopt it without friction. Our recent announcements at the PyTorch Conference reinforce our commitment to making the Google TPU software ecosystem an open, developer-friendly environment for the PyTorch community. Notably, TorchTPU, our native PyTorch stack for Google TPUs, is now available as open source on GitHub. You can now train, serve, and deploy models on TPUs using the tools you already rely on, including vLLM, SGLang, llm-d, TorchTitan, Ray, TorchRec, Hugging Face, LiteRT.  

This portability lets teams assign each workload to the accelerator that delivers the lowest cost per token or fastest time to train, without rewriting model code. And with today’s announcement, they will soon be able to assign those workloads to TPUs on Google Cloud or Oracle infrastructure. 

Expanding the AI infrastructure ecosystem with OCI

Even as TPU adoption on Google Cloud grows, the global demand for AI compute exceeds any single cloud provider’s footprint.Bringing TPUs to OCI gives customers four distinct benefits:

• Data centers engineered for AI density: Google’s TPU systems are designed for direct liquid cooling at rack scale, and OCI’s next-generation data centers are built for exactly that profile. Oracle delivers flexible, durable capacity that can shift between frontier model training and production inference as customer demand changes. Pairing TPU efficiency with Oracle’s innovative power and cooling approaches, helps bring energy-efficient AI-compute capacity online rapidly, and at global scale.

• Secure, high-performance networking with Oracle Acceleron: While TPUs move chip-to-chip traffic over ICI and OCS, Oracle Acceleron provides the networking foundation that connects TPU capacity to the rest of OCI. Acceleron’s  SmartNIC with hardware-level tenant isolation, High Performance Fabric, and Zero Trust Packet Routing moves data between Oracle databases, storage, and TPU clusters with high throughput and low latency, without taxing host compute and without weakening security boundaries.

• Proximity to enterprise data: Oracle databases are often the system-of-record for enterprise organizations. Offering TPUs directly within OCI lets organizations run fine-tuning and inference next to their existing enterprise data without costly cross-cloud transfers.

• Multi-architecture choice: Providing TPUs alongside GPUs inside OCI gives teams the flexibility to assign each AI workload stage to the optimal accelerator for cost and performance.

By bringing Google TPUs to Oracle Cloud Infrastructure, Google Cloud and Oracle are opening up over a decade of purpose-built AI systems engineering to a broader ecosystem of builders. When customers have more choices of purpose-built compute across more clouds, the entire industry builds faster and operates more efficiently. We can’t wait to see what you build.