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Choose a hardware path based on the checkpoint
Start by checking the weight format of the checkpoint you intend to serve. SGLang’s DeepSeek-V3 cookbook recommends FP8 and states: “The official DeepSeek-V3 checkpoint is already in FP8 format — do not add –quantization fp8 when serving it.” That instruction applies to the official FP8 checkpoint; do not assume it applies to a converted or differently quantized model.
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| Weight format | Documented GPU configuration | Arrangement and workload notes |
|---|---|---|
| FP8 | 8× H200; 8× B200; or 8× MI300X | SGLang cookbook examples. The source does not specify a node arrangement for each listed 8-GPU option or tie them to a particular workload objective. |
| FP8 | 2×8× H100, H800, or H20 | SGLang cookbook example. The 2×8 notation indicates two groups of eight GPUs; consult the cookbook for the topology and launch settings relevant to the version you use. |
| BF16 | Larger multi-GPU configurations | The cookbook lists separate BF16 configurations. For one A100 conversion path, the deployment README gives an example of four nodes, each with eight 80 GB A100 GPUs; see the section below. |
| INT8 and other quantized formats | Separate configurations are documented | The cookbook lists configurations for these formats, but the specific GPU counts and arrangements are not stated here. Choose the configuration for the exact format rather than inferring it from the FP8 examples. |
These configurations are SGLang documentation guidance, not a complete procurement specification. The cited pages do not establish current GPU pricing or availability, storage and networking requirements for every deployment, or performance guarantees for your system.
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SGLang’s deployment README describes converting the FP8 checkpoint to BF16 for an A100 deployment. Its example uses four nodes with eight 80 GB A100 GPUs per node and describes the converted model as over 1.3 TB. This is one documented route, not a requirement for all deployments. Use it only if you are following that A100/BF16 path, and plan storage and transfer capacity around the converted checkpoint.
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Launch a DeepSeek-V3 server
The SGLang deployment README provides a Docker example with eight-way tensor parallelism. It uses the rolling latest image tag, so the command and image contents can change over time. For reproducibility, check the current DeepSeek-V3 cookbook and deployment README, then select and record a specific compatible image or package version before deploying.
docker run --rm --gpus all --shm-size 32g -p 30000:30000 lmsysorg/sglang:latest
python3 -m sglang.launch_server
--model deepseek-ai/DeepSeek-V3
--tp 8
--trust-remote-code
--port 30000This is the README’s eight-way tensor-parallel example, not a universal command for every hardware configuration. Match --tp and the rest of the launch settings to your selected hardware and checkpoint. The SGLang quickstart also describes a Python-environment installation route; follow its current instructions if you are not using Docker.
- Prepare the deployment environment. Confirm that the host and container can access the GPUs you plan to use, and that your chosen SGLang version supports the selected hardware and model format.
- Start the server. Use the configuration appropriate to your checkpoint. The command above is a documented FP8-oriented eight-way launch example; do not add
--quantization fp8for the official FP8 checkpoint. - Wait for model loading to finish. If loading is slow and the distributed process times out, the cookbook documents
--dist-timeout 3600as an adjustment. Confirm that option’s spelling and suitability against the SGLang version you are running before adding it. - Check the server endpoint. Once the server reports that it is ready, send a chat-completions request as shown below.
curl http://localhost:30000/v1/chat/completions
-H 'Content-Type: application/json'
-d '{
"model": "deepseek-ai/DeepSeek-V3",
"messages": [
{"role": "user", "content": "Reply with a short greeting."}
],
"temperature": 0,
"max_tokens": 64
}'A successful response containing an assistant message confirms that the endpoint is serving a completion. If the request fails, first check the server logs for model-load errors, confirm the endpoint and port, and verify that the request’s model value matches the model name exposed by your running server.
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Select parallelism for your serving goal
Tensor parallelism distributes model computation across GPUs. The deployment example uses --tp 8; use the degree supported by your selected configuration rather than treating eight as a general requirement.
Data-parallel attention is a separate choice aimed at throughput. The cookbook recommends it for high-throughput serving with large batches and cautions against it for low-latency, small-batch workloads. Use the cookbook’s workload-specific examples to choose settings, then evaluate them under the batch sizes and concurrency your application actually expects.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Enable reasoning or tool-call parsing only when needed
Basic text generation does not require the optional parser settings. If your application needs SGLang to handle the corresponding structured behavior, the cookbook documents these examples:
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- Reasoning parser: add
--reasoning-parser deepseek-v3. - Tool-call parser: use
--tool-call-parser deepseekv3together with a chat template, following the cookbook’s example for the template setting.
Parser names and launch options are version-sensitive. Confirm the exact flags and template supported by the SGLang release you deploy.
Measure the deployment with a representative workload
A successful chat request checks basic serving, not capacity or latency. SGLang documents bench_serving for serving benchmarks and separate accuracy and latency evaluation commands. Use the commands and input formats from the documentation for your installed version.
- Record the GPU model and count, node arrangement, SGLang version, checkpoint format, and launch settings.
- Use representative prompt lengths, output lengths, batch sizes, and concurrency; results from a different workload are not a reliable forecast.
- Compare throughput and latency together. A configuration tuned for large batches and throughput may not be appropriate for a small-batch, latency-sensitive service.
The cookbook includes benchmark results for DeepSeek-V3 on 8× AMD MI300X with tensor parallelism 8 and SGLang 0.5.7, using its stated prompt and concurrency setup. Those results describe that specific test environment, not expected performance on other hardware. The cookbook does not identify a publication year for those results.
Check the current SGLang documentation before pinning a deployment
The SGLang DeepSeek-V3 cookbook and deployment README are rolling project documentation. Image tags, supported hardware, configuration examples, and flags can change. Verify the current model-specific cookbook and deployment instructions before pinning a release or using a command in production.
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