Update toplevel readme

Signed-off-by: Sihan Wang <[email protected]>

README.md

@@ -15,10 +15,11 @@ a variety of open source LLMs, built on [Ray Serve](https://docs.ray.io/en/lates
 - Fully supporting multi-GPU & multi-node model deployments.
 - Offering high performance features like continuous batching, quantization and streaming.
 - Providing a REST API that is similar to OpenAI's to make it easy to migrate and cross test them.
+- Supporting multiple LLM backends out of the box, including [vLLM](https://github.com/vllm-project/vllm) and [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM).
 
 In addition to LLM serving, it also includes a CLI and a web frontend (Aviary Explorer) that you can use to compare the outputs of different models directly, rank them by quality, get a cost and latency estimate, and more.
 
-RayLLM supports continuous batching and quantization by integrating with [vLLM](https://github.com/vllm-project/vllm). Continuous batching allows you to get much better throughput and latency than static batching. Quantization allows you to deploy compressed models with cheaper hardware requirements and lower inference costs. See [quantization guide](models/continuous_batching/quantization/README.md) for more details on running quantized models on RayLLM. 
+RayLLM supports continuous batching and quantization by integrating with [vLLM](https://github.com/vllm-project/vllm). Continuous batching allows you to get much better throughput and latency than static batching. Quantization allows you to deploy compressed models with cheaper hardware requirements and lower inference costs. See [quantization guide](models/continuous_batching/quantization/README.md) for more details on running quantized models on RayLLM.
 
 RayLLM leverages [Ray Serve](https://docs.ray.io/en/latest/serve/index.html), which has native support for autoscaling
 and multi-node deployments. RayLLM can scale to zero and create
@@ -368,4 +369,4 @@ Feel free to post an issue first to get our feedback on a proposal first, or jus
 
 We use `pre-commit` hooks to ensure that all code is formatted correctly.
 Make sure to `pip install pre-commit` and then run `pre-commit install`.
-You can also run `./format` to run the hooks manually.
+You can also run `./format` to run the hooks manually.