Minor

Signed-off-by: WoosukKwon <woosuk.kwon@berkeley.edu>

_posts/2025-01-26-v1-alpha-release.md

@@ -112,7 +112,7 @@ The final piece of the puzzle for vLLM V1 was integrating [FlashAttention 3](htt
 
 # Performance
 
-Thanks to the extensive architectural enhancements, vLLM V1 achieves state-of-the-art throughput and latency, delivering up to **1.7x** higher throughput compared to V0 (*without multi-step scheduling*).
+Thanks to the extensive architectural enhancements, vLLM V1 achieves state-of-the-art throughput and latency, delivering up to **1.7x higher throughput** compared to V0 (*without multi-step scheduling*).
 These dramatic performance gains stem from comprehensive CPU overhead reductions across the entire stack.
 The improvements are even more pronounced for vision-language models (VLMs) like Qwen2-VL, thanks to V1's enhanced support for VLMs.
 
@@ -185,7 +185,7 @@ The V1 re-architecture is a continued joint effort across the entire vLLM team a
 - [Tyler Michael Smith](https://github.com/tlrmchlsmth) implemented the tensor parallelism support with Python multiprocessing.
 - [Rui Qiao](https://github.com/ruisearch42) implemented the tensor parallelism support with Ray and is implementing pipeline parallelism support.
 - [Lucas Wilkinson](https://github.com/LucasWilkinson) added support for FlashAttention 3.
-- [Alexander Matveev](https://github.com/alexm-redhat) implemented the optimized preprocessor for multimodal inputs.  
+- [Alexander Matveev](https://github.com/alexm-redhat) implemented the optimized preprocessor for multimodal inputs and is implementing TPU support.
 - [Sourashis Roy](https://github.com/sroy745) implemented the logit penalties in the sampler.
 - [Cyrus Leung](https://github.com/DarkLight1337) led the MLLM input processing refactoring effort and helped its integration to V1.
 - [Russell Bryant](https://github.com/russellb) addressed several multiprocess-related issues.