Merge pull request #302 from runpod-workers/feat/0.22.1

feat: upgrade vllm to 0.22.1

.runpod/README.md

@@ -6,7 +6,7 @@ Run LLMs using [vLLM](https://docs.vllm.ai) with an OpenAI-compatible API
 
 [![RunPod](https://api.runpod.io/badge/runpod-workers/worker-vllm)](https://www.runpod.io/console/hub/runpod-workers/worker-vllm)
 
-Current vLLM version: [0.20.2](https://github.com/vllm-project/vllm/releases/tag/v0.20.2)
+Current vLLM version: [0.22.1](https://github.com/vllm-project/vllm/releases/tag/v0.22.1)
 
 ---
 

Dockerfile

@@ -8,9 +8,20 @@ ENV PATH="/root/.local/bin:$PATH"
 
 RUN ldconfig /usr/local/cuda-13.0/compat/
 
+# nixl_ep PyPI wheels are compiled against CUDA 12.x and require libcudart.so.12.
+# CUDA 13 runtime is ABI-compatible with CUDA 12, so symlinking is safe.
+# Symlink into /usr/local/cuda/lib64 (already in LD_LIBRARY_PATH) so the linker
+# finds it by filename scan rather than relying on ldcache SONAME lookup.
+RUN ln -sf /usr/local/cuda/lib64/libcudart.so.13 /usr/local/cuda/lib64/libcudart.so.12 && ldconfig
+
+# CUDA 13.0 containers return libs to /usr/local/nvidia/lib64 so container
+# providers (RunPod, Lambda, etc.) can mount host drivers there consistently.
+# See: https://github.com/vllm-project/vllm/issues/18859
+ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
+
 # Install vLLM with FlashInfer - use CUDA 130 PyTorch wheels
 RUN uv pip install --system "packaging>=24.2" && \
-    uv pip install --system "vllm[flashinfer]==0.20.2" && \
+    uv pip install --system "vllm[flashinfer]==0.22.1" && \
     uv pip install --system git+https://github.com/deepseek-ai/DeepGEMM.git@714dd1a4a980f7937a74343d19a8eba4fe321480 --no-build-isolation
 
 # Install additional Python dependencies (after vLLM to avoid PyTorch version conflicts)

README.md

@@ -8,7 +8,7 @@ Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the [vLLM](https:
 
 ![vLLM worker banner](https://image.runpod.ai/preview/vllm/vllm-banner.png)
 
-Current vLLM version: [0.20.2](https://github.com/vllm-project/vllm/releases/tag/v0.20.2)
+Current vLLM version: [0.22.1](https://github.com/vllm-project/vllm/releases/tag/v0.22.1)
 
 
 > Check out our Load Balancer implementation here: [vLLM Load Balancer](https://github.com/runpod-workers/vllm-loadbalancer-ep)

builder/requirements.txt

@@ -3,7 +3,7 @@ pandas
 pyarrow
 runpod==1.9.1
 huggingface-hub
-lmcache==0.4.5
+lmcache==0.4.6
 packaging>=24.2
 typing-extensions>=4.8.0
 pydantic

configs/llama/llama3.1_8b_instruct.yaml

@@ -0,0 +1,10 @@
+model: meta-llama/Llama-3.1-8B-Instruct
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+quantization: fp8
+kv-cache-dtype: fp8
+enforce-eager: false
+enable-prefix-caching: true
+speculative-config: '{"model":"RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'

configs/qwen/qwen3_8b.yaml

@@ -0,0 +1,10 @@
+model: Qwen/Qwen3-8B
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+quantization: fp8
+kv-cache-dtype: fp8
+enforce-eager: false
+enable-prefix-caching: true
+speculative-config: '{"model":"RedHatAI/Qwen3-8B-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'