CP shared-KV bs>1 batching was only bounded by request count, extend tokens, and cached tokens. That left temporary GPU buffers such as MLA/index materialization, remap metadata, logits windows, and transfer descriptors implicit, and raw extend-token limits could exceed the active chunked-prefill budget.\n\nThis adds an explicit max-buffer-size admission gate with a CPU-only stream-aware estimator, wires it through PrefillAdder/Scheduler, performs a startup CUDA smoke allocation when configured, and reports the estimate in the scheduler admission benchmark. When chunked prefill is active, the effective CP extend-token limit is capped by the current chunk budget so the CP path does not advertise unreachable batch capacity or lift max-prefill-tokens too far.\n\nConstraint: Admission estimation must stay CPU-only on the scheduler hot path; CUDA allocation is limited to startup smoke checking.\nConstraint: Single oversized requests must still be allowed to run alone to avoid scheduler deadlock.\nRejected: Rely only on --max-prefill-tokens | it does not reliably bound the first oversized request and does not model cache-hit/load-back pressure.\nRejected: Let CP extend limit exceed chunked-prefill size | it creates an unreachable effective capacity and misleading budget lift.\nConfidence: medium\nScope-risk: moderate\nDirective: If bs>1 L1 prefetch is enabled later, update CPSharedKVPrefillBufferEstimatorContext.bs_gt1_l1_prefetch_enabled and include the live prefetch dense buffers in overlap windows.\nTested: local py_compile for touched files\nTested: local PYTHONPATH=python pytest -q test/registered/unit/managers/test_cp_shared_kv_prefill_buffer_estimator.py (4 passed)\nTested: remote cjy-glm5-new targeted pytest for new server_args, PrefillAdder, estimator, and benchmark cases (10 passed)\nTested: remote cjy-glm5-new PYTHONPATH=python pytest -q test/registered/unit/managers/test_cp_shared_kv_prefill_buffer_estimator.py test/registered/unit/managers/test_prefill_adder.py test/registered/unit/managers/test_prefill_scheduler_admission_bench.py (29 passed before chunk cap, then test_prefill_adder.py 21 passed after chunk cap)\nNot-tested: full server_args suite because existing TestPrepareServerArgs tries to reach HuggingFace and fails under container DNS/network\nNot-tested: GLM5 ETE smoke with --cp-shared-kv-prefill-max-buffer-size
Add an offline benchmark that reuses PrefillAdder to model how L1 cached tokens, L2 HiCache hits, and extend tokens shape CP shared-KV prefill batch admission. The tool makes scheduler stop reasons and fake L2 load-back capacity pressure observable without starting a model.
Constraint: The benchmark must stay CPU/offline and avoid depending on CUDA execution or live services.
Constraint: L2 cached tokens are modeled as host_hit_length, so successful load-back both increases prefix_len and consumes fake L1 capacity.
Rejected: Build an ETE benchmark first | too slow for isolating scheduler admission behavior.
Rejected: Reimplement scheduler logic from scratch | would drift from PrefillAdder semantics.
Confidence: high
Scope-risk: narrow
Directive: Treat duration_us as Python admission overhead only; it is not an ETE latency metric.
Tested: Remote pytest test/registered/unit/managers/test_prefill_scheduler_admission_bench.py: 4 passed as part of 6 targeted tests.
Tested: Remote synthetic benchmark run with --cp-max-total-cached-tokens showed second 4096-token cached request stopped with OTHER.
Not-tested: Real traffic trace import from production logs.
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