Announcing New vLLM and SGLang Inference Engines on TPU

This post announces native PyTorch backends for vLLM and SGLang powered by TorchTPU, enabling developers to run and optimize serving workloads using familiar PyTorch tools. Built in collaboration with open-source project maintainers, these engines include optimized TPU kernel implementations, production-ready features like KV cache management with TPU Sync, and disaggregated serving. Public benchmarks demonstrate state-of-the-art performance and industry-leading cost efficiency on Ironwood TPU across long-context and agentic workloads.