LLM INFERENCE
Default Port: 8000Minimum 24GB VRAMBest Cloud GPU Providers for vLLM High-Throughput Server
vLLM is the leading open-source serving engine for LLMs (DeepSeek, Llama 3, Qwen 2.5). Its PagedAttention algorithm delivers 2-4x higher throughput than standard Hugging Face TGI.
Verified 1-Click Cloud Launchers for vLLM High-Throughput Server
Click to launch directly with pre-installed CUDA drivers, PyTorch, and template weights
Direct Docker Run Command
docker run --gpus all --ipc=host -d -p 8000:8000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
vllm/vllm-openai:latest \
--model meta-llama/Llama-3.3-70B-Instruct \
--tensor-parallel-size 4 \
--gpu-memory-utilization 0.95Deployment Best Practices & Gotchas:
- Set --gpu-memory-utilization to 0.95 to maximize KV cache allocation.
- For multi-GPU tensor parallelism, ensure instances feature NVLink interconnect to prevent bus choking.
- Set HUGGING_FACE_HUB_TOKEN in environment to download gated models like Llama 3.
Recommended GPU Hardware for vLLM High-Throughput Server
To achieve reliable throughput without running into CUDA Out of Memory (OOM) errors, we recommend deploying on one of the following verified GPU models:
A100 SXM (80GB)H100 SXM5 (80GB)L40S (48GB)