March 2026 (vllm-project/tpu-inference): Focused on reliability and accurate performance measurement in CI. The primary delivery was aligning Async Scheduler CI test thresholds with real CI environments, which yields more trustworthy performance signals and tighter release gates. This work, reflected in commit 5027394eea5ce06b27e5a06506a5fa421f27ae90, reduces CI noise and accelerates iteration by surfacing true regressions sooner.

December 2025: Documentation update to align the vLLM Docker image version in the README for AI-Hypercomputer/tpu-recipes. This change ensures reproducible results and reduces onboarding friction by keeping users aligned with the recommended container image version.

October 2025 monthly summary for vllm-project/tpu-inference. Delivered an Asynchronous TPU Inference Scheduler, enabling concurrent request processing and significantly boosting throughput. Implemented via updates to the compilation manager and the TPU model runner to support asynchronous operations and token substitutions. Added tests to validate performance gains and output correctness against the synchronous scheduler. The work is tracked under commit ae065847bdc055c3f9dd40cf6ba8030ec99b9e08 ([Feature] Code implementation of Async Scheduler #924).