Skip to main content
A large matrix-compute specification does not tell you how quickly a TPU will serve your model. Evaluate Google TPU inference by matching the model, serving backend, memory budget, and slice topology to your request mix. Measure startup and steady-state performance separately, then compare against a GPU endpoint at the same quality and latency target.

​
What does a TPU change in the computation?

TPUs use matrix-multiply units built around systolic arrays: data moves through connected multiply-accumulate units to perform matrix operations. High-bandwidth memory holds data outside those compute units, while other units handle operations beyond matrix multiplication. Google’s architecture guide explains the components and interconnect. That specialization matters only when the runtime maps the model’s operations onto it efficiently. Attention, routing, data movement, and synchronization still contribute to inference time. Long-prompt prefill and low-concurrency decode can use the same accelerator quite differently.

​
Why does the slice topology matter?

A TPU slice groups connected chips. The arrangement determines how model partitions exchange data and how work spans hosts. Record the precise accelerator generation, chip count, topology, and memory layout when comparing results. Adding chips can make a larger model fit, but introduces communication that a smaller layout may avoid. Compare a useful unit of serving capacity, such as requests per second within your latency target, rather than treating every TPU label as equivalent to one GPU.

​
Can you use vLLM on TPU?

Google documents a tpu-inference plugin for vLLM and paths involving JAX and PyTorch models in its Cloud TPU inference guide. Check the current plugin’s supported models, hardware, and feature coverage. This is a TPU-specific execution path; an existing CUDA image is not the deployment artifact to reuse. Start with the upstream TPU quickstart, pin the relevant versions, and confirm the exact model implementation. Then test the application contract: streaming, tool calls, structured output, stop conditions, and any multimodal inputs.

​
What should a migration test include?

Measure compilation and warm-up before measuring sustained inference. Test the range of prompt lengths and concurrent requests the application produces, watching for recompilation or inefficient shape handling in the selected backend. Keep model quality and output budgets fixed. Include quota, regional capacity, host resources, and networking in the evaluation. If your application and data remain on AWS, a Google Cloud deployment introduces a cross-cloud request path; measure that path from the application’s location and account for its transfer costs. The benchmarking guide provides comparable request-level metrics. This page explains Google Cloud TPU evaluation and does not establish a TPU deployment path through Tensorfuse’s AWS configuration.

AWS Neuron inference

Compare another accelerator software stack and its compilation requirements.

Choose inference hardware

Match hardware to the part of inference that limits your workload.

​
Put this into practice

Follow the vLLM on Google TPU how-to to install the backend, verify devices, and serve a local model endpoint.