[Docs] Update readme (#7316)

docs/source/index.rst

@@ -31,8 +31,10 @@ vLLM is fast with:
 * Efficient management of attention key and value memory with **PagedAttention**
 * Continuous batching of incoming requests
 * Fast model execution with CUDA/HIP graph
-* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, `SqueezeLLM <https://arxiv.org/abs/2306.07629>`_, FP8 KV Cache
-* Optimized CUDA kernels
+* Quantization: `GPTQ <https://arxiv.org/abs/2210.17323>`_, `AWQ <https://arxiv.org/abs/2306.00978>`_, INT4, INT8, and FP8
+* Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
+* Speculative decoding
+* Chunked prefill
 
 vLLM is flexible and easy to use with:
 
@@ -41,9 +43,9 @@ vLLM is flexible and easy to use with:
 * Tensor parallelism and pipeline parallelism support for distributed inference
 * Streaming outputs
 * OpenAI-compatible API server
-* Support NVIDIA GPUs and AMD GPUs
-* (Experimental) Prefix caching support
-* (Experimental) Multi-lora support
+* Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
+* Prefix caching support
+* Multi-lora support
 
 For more information, check out the following:
 
@@ -53,7 +55,6 @@ For more information, check out the following:
 * :ref:`vLLM Meetups <meetups>`.
 
 
-
 Documentation
 -------------