LLM INFERENCE
Default Port: 30000Minimum 24GB VRAMBest Cloud GPU Providers for SGLang Inference Engine
SGLang is designed for complex LLM workflows and reasoning models like DeepSeek-R1. Its RadixAttention mechanism automatically reuses KV cache across multi-turn prompts and tree search, achieving up to 5x higher throughput than standard vLLM on agentic workloads.
Verified 1-Click Cloud Launchers for SGLang Inference Engine
Click to launch directly with pre-installed CUDA drivers, PyTorch, and template weights
Direct Docker Run Command
docker run --gpus all -d -p 30000:30000 \
--ipc=host \
-v /root/.cache/huggingface:/root/.cache/huggingface \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --port 30000 --host 0.0.0.0Deployment Best Practices & Gotchas:
- Enable `--enable-torch-compile` for 15-20% higher decoding token velocity.
- Use `--mem-fraction-static 0.9` to maximize KV cache allocation for long reasoning chains.
- Multi-GPU setups require NVLink interconnect for tensor parallelism to prevent PCIe all-reduce choke.
Recommended GPU Hardware for SGLang Inference Engine
To achieve reliable throughput without running into CUDA Out of Memory (OOM) errors, we recommend deploying on one of the following verified GPU models:
RTX 4090 (24GB)A100 SXM (80GB)8x H100 SXM (640GB)