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@@ -18,14 +18,20 @@ Kernel Library for LLM Serving
 
 FlashInfer is a library and kernel generator for Large Language Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, SparseAttention, PageAttention, Sampling, and more. FlashInfer focuses on LLM serving and inference, and delivers state-of-the-art performance across diverse scenarios.
 
-The unique features of FlashInfer include:
-1. **Comprehensive Attention Kernels**: Attention kernels that cover all the common use cases of LLM serving, including *single-request* and *batching* versions of *Prefill*, *Decode*, and *Append* kernels, on different formats of KV-Cache (Padded Tensor, Ragged Tensor, and Page Table).
-2. **Optimized Shared-Prefix Batch Decoding**: FlashInfer enhances shared-prefix batch decoding performance through *cascading*, resulting in an impressive **up to 31x speedup** compared to the baseline vLLM PageAttention implementation (for long prompt of 32768 tokens and large batch size of 256).
-3. **Accelerate Attention for Compressed/Quantized KV-Cache**: Modern LLMs are often deployed with quantized/compressed KV-Cache to reduce memory traffic. FlashInfer accelerates these scenarios by optimizing performance for *Grouped-Query Attention*, *Fused-RoPE Attention* and *Quantized Attention*.
+Check our [v0.2 release blog](https://flashinfer.ai/2024/12/16/flashinfer-v02-release.html) for new features!
+
+The core features of FlashInfer include:
+1. **Efficient Sparse/Dense Attention Kernels**: Efficient single/batch attention for sparse(paged)/dense KV-storage on CUDA Cores and Tensor Cores (both FA2 & FA3) templates. The vector-sparse attention can achieve 90% of the bandwidth of dense kernels with same problem size.
+2. **Load-Balanced Scheduling**: FlashInfer decouples `plan`/`run` stage of attention computation where we schedule the computation of variable-length inputs in `plan` stage to alleviate load-imbalance issue.
+3. **Memory Efficiency**: FlashInfer offers [Cascade Attention](https://docs.flashinfer.ai/api/cascade.html#flashinfer.cascade.MultiLevelCascadeAttentionWrapper) for hierical KV-Cache, and implements Head-Query fusion for accelerating Grouped-Query Attention, and efficent kernels for low-precision attention and fused-RoPE attention for compressed KV-Cache.
+4. **Customizable Attention**: Bring your own attention variants through JIT-compilation.
+5. **CUDAGraph and torch.compile Compatibility**: FlashInfer kernels can be captured by CUDAGraphs and torch.compile for low-latency inference.
+6. **Efficient LLM-specific Operators**: High-Performance [fused kernel for Top-P, Top-K/Min-P sampling](https://docs.flashinfer.ai/api/sampling.html) without the need to sorting.
 
 FlashInfer support PyTorch, TVM and C++ (header-only) APIs, and can be easily integrated into existing projects.
 
 ## News
+- [Dec 16, 2024] [Blog Post](https://flashinfer.ai/2024/12/16/flashinfer-v02-release.html) FlashInfer 0.2 - Efficient and Customizable Kernels for LLM Inference Serving
 - [Sept 2024] We've launched a [Slack](https://join.slack.com/t/flashinfer/shared_invite/zt-2r93kj2aq-wZnC2n_Z2~mf73N5qnVGGA) workspace for Flashinfer users and developers. Join us for timely support, discussions, updates and knowledge sharing!
 - [Jan 31, 2024] [Blog Post](https://flashinfer.ai/2024/01/08/cascade-inference.html) Cascade Inference: Memory-Efficient Shared Prefix Batch Decoding
 - [Jan 31, 2024] [Blog Post](https://flashinfer.ai/2024/01/03/introduce-flashinfer.html) Accelerating Self-Attentions for LLM Serving with FlashInfer