THE SOURCE IN CONTEXT
An upstream reference for model-serving deployments. Begin with the project documentation, then verify the exact backend, model and container combination you intend to use.
Where does SGLang fit?
SGLang is a model-serving framework with its own scheduler, kernel and deployment integrations. Its upstream overview is a starting point; a model-specific cookbook supplies the detailed launch route. A matching model name is insufficient when the backend, quantization or container architecture differs.
Source: sgl-project · pinned document
Why keep cache and speculative decoding explicit?
A reused prefix can replace measured prefill with a lookup. A draft model consumes memory and may have a model-specific acceptance pattern. Record whether caching is on, which prompts warm it, and the exact draft artifact and decoding policy. The two-Spark report in this collection shows why these controls materially change reported served rate.
Source: sgl-project · pinned document
What changes for distributed serving?
Capture the tensor/data/expert parallelism, interconnect, NCCL transport, per-node image and kernel cache state. A fast standalone link test does not prove that a serving container inherited the same network configuration. The DGXArley transport reference and two-Spark audit describe those distinctions without certifying another cluster.
Source: sgl-project · pinned document
How should a reproducible trial be scoped?
First establish architecture and artifact support with a small request. Then record a fixed workload, input/output lengths, concurrency, sampling, warmup, cache, timing boundaries and failures. Keep failed configurations with their conditions. A later successful setting is a new observation rather than proof that the original attempt was valid.
Source: sgl-project · pinned document
AI-assisted source reading and original editorial synthesis; no independent model execution.