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To serve DeepSeek-R1 with SGLang, install a platform-compatible SGLang build, then launch the model with python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1 --host 0.0.0.0 --port 30000. Send a chat-completions request to http://localhost:30000/v1/chat/completions to check that the server responds. Hardware needs depend on the checkpoint, quantization, and accelerator: SGLang’s published Ascend examples, for instance, cover two specific quantized variants and are not universal GPU requirements.
Check the model and hardware before launching
DeepSeek-R1 is a mixture-of-experts model with 671 billion total parameters and 37 billion active parameters, according to SGLang’s Ascend deployment tutorial. Those figures describe the model; they do not by themselves specify the number or type of devices required to serve it. Choose the exact checkpoint and quantization, accelerator platform, and deployment topology before estimating memory or installing a platform-specific build.
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The tutorial gives these minimum recommended configurations for two quantized DeepSeek-R1-0528 variants on Ascend. They are examples for those variants and that platform, not sizing guidance for full-precision R1, NVIDIA GPUs, or other quantizations.
| Checkpoint and quantization | Weight size stated by SGLang | Ascend device-memory configuration stated by SGLang |
|---|---|---|
| DeepSeek-R1-0528-W4A8 | 376 GB | 8 devices with 64 GB each; --tp-size 8 |
| DeepSeek-R1-0528-W8A8 | 647 GB | 16 devices with 64 GB each; --tp-size 16 |
The tutorial describes these as minimum recommended configurations and points to separate best-practice configurations that may use more devices. Weight size is not the same as total serving-memory demand: these figures alone do not establish memory available for runtime needs or the KV cache. Confirm requirements for the selected checkpoint and platform rather than treating the examples as a general minimum.
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Install SGLang for the target platform
Follow SGLang’s deployment guidance for the accelerator you intend to use, and confirm compatibility before starting the server. The general launch command below comes from the DeepSeek-R1 model card. It does not set a universal GPU count or establish a current minimum NVIDIA configuration.
SGLang’s Ascend tutorial says it was validated with SGLang v0.5.13 and recommends v0.5.13 or later for newer features such as prefill-decode disaggregation and speculative decoding. That version guidance is tied to the tutorial’s newer features; it is not a claim that v0.5.13 is the latest release or a universal minimum for every launch.
Start the DeepSeek-R1 server
Run this on the machine that will host inference after installing a compatible SGLang build:
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python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1 --host 0.0.0.0 --port 30000The model path is the model ID used in the model-card example. Keep the model and tokenizer revisions consistent with the repository and serving workflow you selected. Binding to 0.0.0.0 makes the service listen on all network interfaces; restrict access with appropriate network controls rather than exposing an unauthenticated inference endpoint publicly.
Docker starting point
The model card’s Docker example uses GPU access, shared memory, IPC, a published port, and a mounted Hugging Face cache. Those options provide a starting point for running the example in a container; the exact container image and full Docker command are not specified here. Adapt the setup to the installed SGLang version, accelerator, cache location, and selected model.
Verify the server with a chat-completions request
Once the server is running, send a request from the same machine to its OpenAI-compatible chat-completions endpoint:
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curl http://localhost:30000/v1/chat/completions
-H 'Content-Type: application/json'
-d '{
"model": "deepseek-ai/DeepSeek-R1",
"messages": [
{"role": "user", "content": "Explain what a mixture-of-experts model is in one sentence."}
]
}'A successful response confirms that the endpoint accepted the request and returned a completion. If the request fails, first check that the launch process is still running, that the request uses the same port as the launch command, and that the model ID and checkpoint are valid for the serving setup. For a client on another machine, use the server’s reachable address instead of localhost and ensure network access to the chosen port is permitted.
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To expose Prometheus metrics, start SGLang with --enable-metrics. The metrics endpoint for this example’s port is http://localhost:30000/metrics. Use the measurements to observe the running service; a successful API response alone does not show whether the server meets a workload’s latency or throughput needs.
SGLang’s serving benchmark guide describes measuring throughput, time to first token, inter-token latency, and per-request end-to-end latency. For a useful deployment decision, benchmark with representative prompt and output lengths, concurrency, and streaming settings. Results from an example or documentation are not measurements of your own hardware or workload.
When to separate prefill and decode
Prefill processes the input sequence; decode generates output tokens while managing the KV cache. SGLang’s prefill-decode (PD) disaggregation documentation describes separating these phases so they can be optimized independently, and names Mooncake and NIXL as transfer engines.
PD disaggregation adds components and a networking setup to manage. Consider it when workload characteristics justify operating prefill and decode separately, then benchmark the resulting system against unified serving. The documentation does not establish a universal performance gain for DeepSeek-R1.
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