0.16.0 release (#1576)

Signed-off-by: Roger Wang <hey@rogerw.io>

docs/getting_started/installation/gpu/cuda.inc.md

@@ -40,7 +40,7 @@ If you do not need to modify source code of vLLM, you can directly install the s
 uv pip install vllm==0.16.0 --torch-backend=auto
 ```
 
-The release 0.14.0 of vLLM is based on PyTorch 2.9.0 which requires CUDA 12.9 environment.
+The release 0.16.0 of vLLM is based on PyTorch 2.9.0 which requires CUDA 12.9 environment.
 
 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -91,7 +91,7 @@ docker run --runtime nvidia --gpus 2 \
     --env "HF_TOKEN=$HF_TOKEN" \
     -p 8091:8091 \
     --ipc=host \
-    vllm/vllm-omni:v0.14.0 \
+    vllm/vllm-omni:v0.16.0 \
     --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8091
 ```
 

docs/getting_started/installation/gpu/rocm.inc.md

@@ -130,7 +130,7 @@ docker run --rm \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   -p 8091:8091 \
-  vllm/vllm-omni-rocm:v0.14.0 \
+  vllm/vllm-omni-rocm:v0.16.0 \
   --model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 
@@ -149,7 +149,7 @@ docker run --rm -it \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   --entrypoint bash \
-  vllm/vllm-omni-rocm:v0.14.0
+  vllm/vllm-omni-rocm:v0.16.0
 ```
 
 # --8<-- [end:pre-built-images]