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To serve DeepSeek-R1 with SGLang, install a platform-compatible SGLang build, choose a checkpoint and accelerator configuration that fit your deployment, then launch the model with SGLang’s server command. The DeepSeek-R1 model card’s general example uses deepseek-ai/DeepSeek-R1 on port 30000; it does not establish a universal GPU count or a current minimum NVIDIA configuration.
What you need to decide before launching
DeepSeek-R1 is a mixture-of-experts model. An SGLang Ascend deployment tutorial describes it as having 671 billion total parameters and 37 billion active parameters. The active-parameter count describes the model’s MoE architecture; it is not a statement that the full model’s weights fit in memory equal to 37 billion parameters.
Before choosing hardware or a launch configuration, identify the exact checkpoint, quantization, accelerator platform, and topology. Weight size and device requirements differ across those choices. Keep the model and tokenizer revisions consistent with the repository and serving workflow you select.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Checkpoint and quantization: Confirm whether you are serving the general
deepseek-ai/DeepSeek-R1model or a specific variant, such as one of the quantized Ascend examples below. - Accelerator platform: Check SGLang’s compatibility guidance for the GPUs or accelerators in your server. The Ascend configurations are not NVIDIA requirements.
- Topology: Decide whether the deployment is a single node or spans multiple nodes, and whether it will serve requests with a unified process or separate prefill and decode.
- Workload: Estimate prompt lengths, generated output lengths, concurrency, and whether clients will stream responses. Those factors affect capacity planning and the performance you need to measure.
What hardware do the published Ascend examples specify?
The SGLang Ascend deployment tutorial gives two configurations for named DeepSeek-R1-0528 quantizations. These are platform- and variant-specific guidance, not universal requirements for full-precision R1, NVIDIA hardware, or other quantizations.
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| Model variant | Weight size | Ascend device-memory configuration in the tutorial | Tensor parallel size |
|---|---|---|---|
| DeepSeek-R1-0528-W4A8 | 376 GB | 8 × 64 GB devices | --tp-size 8 |
| DeepSeek-R1-0528-W8A8 | 647 GB | 16 × 64 GB devices | --tp-size 16 |
The tutorial presents these as minimum recommended configurations for those examples and points to separate best-practice configurations that may use more devices. Weight size is not the same as a complete estimate of runtime memory: the cited examples do not establish a general memory budget for other platforms, quantizations, or workloads.
How to launch the SGLang server
Install a compatible SGLang build
Follow the official SGLang deployment guidance for your accelerator platform and confirm compatibility before starting the server. The Ascend tutorial says it was validated with SGLang v0.5.13 and recommends v0.5.13 or later for newer features such as PD disaggregation and speculative decoding. This is the tutorial’s version guidance, not a claim that v0.5.13 is the latest release.
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Start the general model-card example
For the model-card launch path, run:
python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1 --host 0.0.0.0 --port 30000This binds the server to all network interfaces on port 30000. Do not expose that listener beyond the networks you intend to serve; configure your deployment’s network controls accordingly. The command is a starting example, not a complete hardware configuration: choose any additional platform- or topology-specific settings based on your selected checkpoint and deployment.
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Use the model-card Docker pattern if you deploy in a container
The model card’s Docker example uses GPU access (--gpus all), shared memory, IPC, a published server port, and a mounted Hugging Face cache. Those options provide a containerized starting pattern, but the example does not define a universal GPU count or resolve model-specific memory needs. Match the image and runtime setup to the SGLang version and accelerator platform you intend to use.
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How to check that the server responds
Once the process is running, send a chat-completions request to the OpenAI-compatible endpoint at http://localhost:30000/v1/chat/completions. For a local test, use:
curl http://localhost:30000/v1/chat/completions
-H 'Content-Type: application/json'
-d '{
"model": "deepseek-ai/DeepSeek-R1",
"messages": [
{"role": "user", "content": "Explain why the sky appears blue in two sentences."}
]
}'A successful response confirms that the endpoint accepted the request and returned a completion. Check the server logs if it does not respond, and verify that the model identifier, endpoint, port, and selected deployment configuration match the running process before integrating a client.
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How to monitor and benchmark the deployment
Enable Prometheus metrics
Start the server with the --enable-metrics option to expose metrics, then query http://localhost:30000/metrics. The metrics endpoint is useful for observing the running service; make sure your network policy does not expose it unintentionally.
Measure the workload you expect to serve
SGLang’s serving benchmark guide covers throughput, time to first token, inter-token latency, and end-to-end latency per request. Benchmark with representative prompt and output lengths, concurrency, and streaming settings. A result from a different workload is not a reliable forecast for yours, and the existence of benchmark documentation is not itself a benchmark result.
- Throughput helps characterize how much work the server completes over time.
- Time to first token captures the wait before a streamed response begins producing output.
- Inter-token latency helps describe the pacing of generated tokens after output starts.
- Per-request end-to-end latency measures the full time a request takes under the conditions you tested.
When to separate prefill and decode
Prefill processes the input sequence; decode generates output tokens while managing the KV cache. SGLang’s 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 networking considerations. Consider it when your workload and infrastructure justify that complexity, then benchmark the resulting setup against unified serving under the same conditions. The cited guidance does not establish a universal performance gain for DeepSeek-R1.
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