Halkali merkez neighborhood fatih st ozgur apt no 46 , Kucukcekmece , Istanbul , 34303 , TR
SGLang is designed for production-level LLM serving and can expose OpenAI-compatible APIs from single-GPU to distributed clusters. Capacity planning should measure not only tokens/s but TTFT, TPOT and concurrency behavior.
Official SGLang server arguments use `--tp` for tensor parallelism and `--dp` for data parallelism, with the Model Gateway recommended for data parallel setups. `bench_serving` measures production-facing metrics such as TTFT, TPOT, ITL and throughput.
The Model Gateway can route traffic across replicas or data-parallel servers, while GPU servers run the model according to TP/DP topology.
Evaluate first-token latency, inter-token behavior and throughput under concurrency together.
Use TP when the model does not fit one GPU. If it fits but throughput is insufficient, DP may be better. TP and DP can also be combined.
Adapt model name and concurrency to your workload.
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python -m sglang.launch_server --model-path Qwen/Qwen3-8B --host 127.0.0.1 --port 30000python -m sglang.bench_serving --backend sglang --base-url http://127.0.0.1:30000 --num-prompts 100nvidia-smiss -lntp | grep ':30000'Centralize authentication, rate limits, usage quotas, TLS and tenant policies in a gateway or reverse proxy.
Yes. Official documentation supports model serving with OpenAI-compatible APIs.
TP splits model computation across GPUs; DP replicates instances to increase throughput.
Users feel initial responsiveness through TTFT; high overall throughput can still feel slow with poor TTFT.
Share model, GPUs, context, peak concurrency and latency targets; we can design TP/DP and gateway topology.