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An EAGLE draft checkpoint in SGLang must match the particular target model and method implementation.
An EAGLE draft checkpoint in SGLang must match the particular target model and method implementation. An arbitrary small language model cannot replace an artifact trained for that contract. Verify draft provenance, revisions, and candidate-tree settings before load testing. Otherwise, successful file loading can conceal an invalid pairing, while a launch failure may be wrongly blamed on general memory pressure or broken speculative decoding.
Establish the EAGLE contract
The SGLang documentation distinguishes EAGLE, EAGLE3, and STANDALONE. These are not interchangeable names for any additional model. The draft must match the target and expected intermediate-information interface. Preserve the model card or release description establishing that relationship. Similar size or tokenizer behavior alone does not prove compatibility across the complete interaction or the installed runtime’s execution path. Treat the official pairing information as an artifact dependency rather than an informal hint.
Pin tree configuration
Speculative steps and branching determine the candidates verified by the target. Check current parameters and draft-revision controls in server arguments. Changing the tree is a separate experiment, not an invisible side effect of updating a checkpoint. First establish a correct minimal run with one fixed combination. Then vary one setting while retaining the effective configuration. Another benchmark’s tree is not a ready-made recommendation for a different GPU, model, batch composition, or answer length.
Create a compatibility record
Record target repository and commit, draft repository and commit, method, and tree settings. Include the source establishing compatibility and the local validation result. An isolated negative control can verify that an unsuitable artifact produces an intelligible rejection, without exposing production requests. It does not justify using arbitrary pairs. Save tokenizer artifacts and identify whether the chosen implementation expects them from the target or draft repository. An operator should be able to reconstruct the relationship without guessing from checkpoint filenames.
Interpret measurements carefully
After functional acceptance, compare the ordinary target with EAGLE using identical prompts and termination rules. Record acceptance, end-to-end latency, memory, and request concurrency. Accepted-candidate fractions help explain the mechanism but cannot replace the complete serving result. If latency regresses, investigate tree construction and verification costs. If response structure changes, return to the model-template contract instead of tuning for an attractive acceleration factor. Different realized output lengths also require attention before elapsed times are compared.
Preserve the pair through upgrades
Version the pair record and fixtures with the engine. A target update does not automatically preserve compatibility with the previous EAGLE draft; consult the corresponding official description and repeat acceptance. State which sampling and batch modes were actually evaluated. Preserve enough information to restore the previous compatible pair together. Disabling speculation for workloads where its cost is not justified is a valid experimental outcome, not proof that the method is broken. Neither a checkpoint name nor its candidate count establishes universal performance.
Related reading
SGLang or vLLM: compare engines on the same workload; Reproducible inference stacks: preserving a GPU pilot’s result.