The verdict
Google TPU appears in 1 AI-ranked category — best position #3 for ai inference chip.
The only non-GPU silicon running frontier-scale production inference today — mature JAX/XLA and growing vLLM support, strong perf-per-dollar on GCP, and Ironwood is explicitly inference-optimized; assumes the practitioner is willing to run in Google Cloud rather than own hardware
Gemini Delivers the best balance of cost-efficiency, software maturity via PyTorch/XLA and JAX, and cloud accessibility for mainstream LLM deployment, offering a ~4.7x price-performance improvement over previous generations.
GPT The strongest hyperscale option, combining enormous pod-scale compute, high-bandwidth memory, strong dense and MoE performance, and mature JAX/XLA infrastructure for demanding inference fleets.
Where Google TPU falls short, per the models
- GPT Expensive, region- and quota-constrained Google Cloud capacity makes it poor value for ordinary or small deployments.
- Claude GCP-only lock-in — you can't buy one, and porting CUDA-centric stacks still costs real engineering time
- Gemini Locked exclusively to Google Cloud Platform, preventing on-premises deployments or multi-cloud flexibility.
Top alternatives per the models: Groq LPU · Cerebras WSE-3 · AWS Inferentia2 · SambaNova SN50
Rankings are computed from what the models answer, re-polled weekly · raw reasoning shown verbatim · methodology