This is a summarized guide. You can read the full, comprehensive step-by-step tutorial (including the Python TTFT benchmarking script) on GPUYard's Official Blog.
If you want to deploy SGLang on a dedicated GPU server, the short answer is: install the NVIDIA Container Toolkit, pull the official SGLang Docker image, launch it with your model and GPU flags, and connect to it through its OpenAI-compatible API endpoint.
But why choose SGLang over standard serving engines like vLLM? The answer is RadixAttention — a KV cache reuse mechanism that can meaningfully cut Time-to-First-Token (TTFT) for RAG pipelines, agents, and any multi-turn workload with repeated context.
The Core Mechanism: RadixAttention
In a standard transformer serving setup, the key-value (KV) cache for a request is discarded once the response is generated. If the next request shares the same system prompt, the engine still has to recompute the attention states from scratch. That recomputation is pure waste.
RadixAttention solves this by organizing all cached KV states across requests into a radix tree, where common prefixes are stored once and shared across branches.
- Shared system prompts are computed once, not per-request.
- Multi-turn conversations reuse the KV cache from earlier turns.
- TTFT drops sharply on any "warm" request.
Hardware Requirements (VRAM Planning)
Before you touch Docker, budget for the model weights first, then reserve additional VRAM for the KV cache.
| Model Size | Minimum VRAM (approx.) | Recommended GPU(s) |
|---|---|---|
| 8B (Llama-3.1) | 16–24 GB | RTX 4090, L4, A100 (40GB) |
| 32B | 48–64 GB | A100 (80GB), H100 |
| 70B | 140+ GB | 2–4x A100/H100 (--tp 4) |
Step-by-Step Docker Deployment
Your dedicated GPU server needs Ubuntu, NVIDIA drivers, CUDA, and the NVIDIA Container Toolkit. Once those are ready, you can pull and run the official SGLang server image.
Here is the launch command for a Llama-3.1-8B-Instruct model:
docker run --gpus all \
--ipc=host \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=your_huggingface_token" \
lmsysorg/sglang:v0.5.12 \
python3 -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
--host 0.0.0.0 \
--port 30000 \
--mem-fraction-static 0.85
⚠️ A Crucial Production Gotcha
Do not forget the --ipc=host flag!
SGLang relies on shared memory for efficient inter-process communication (especially with multiple GPU workers). Docker's default IPC namespace is often too small — omitting this flag will cause silent crashes or hangs under heavy load.
Also, --mem-fraction-static 0.85 controls what fraction of GPU memory SGLang reserves upfront for model weights and the static KV cache pool. If you hit a "CUDA Out of Memory" error, lower this to 0.80 or 0.75.
What's Next? (Benchmarking TTFT)
Now that your server is running, the best way to prove RadixAttention works is to write a Python script that streams responses and compares the TTFT of a "Cold Cache" vs. a "Warm Cache" using the OpenAI SDK.
I have included the full Python benchmarking script, plus advanced configurations for Multi-GPU Tensor Parallelism (--tp) and Quantization (AWQ/FP8) in the original tutorial.
👉 Read the Full Guide and Get the Benchmarking Script on GPUYard
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