Use tai-kernel for direct page_first_direct per-layer H2D load across MHA, MLA, and NSA indexer pools. This keeps SGLang off the sgl-kernel cudaMemcpyBatchAsync path that crashes on CUDA 13 while preserving fail-fast behavior when the required TAI op is unavailable.
Constraint: remote CUDA 13 stack crashes in sgl-kernel PF->LF direct load via cuMemcpyBatchAsync_v2
Rejected: Silent fallback to sgl-kernel or Python loop | fallbacks would hide either a crash-prone ABI path or a large performance regression
Confidence: high
Scope-risk: moderate
Directive: page_first_direct direct load must remain fail-fast if tai_kernel.nsa_prefill.transfer_kv_per_layer_direct_pf_lf is missing
Tested: remote g0034 PYTHONPATH=python pytest -q test/registered/unit/managers/test_hicache_controller_cp.py: 55 passed, 3 warnings
Tested: remote g0034 CUDA smoke for MLATokenToKVPoolHost.load_to_device_per_layer with direct/page_first_direct passed
Not-tested: full SGLang ETE server after the final commit
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