[Bugfix][Model] Reuse CUDA segments when loading Inkling expert weights

[mo-ke-ke]

Purpose

Fixes #51205.

The NVIDIA Inkling w13 loader dropped the previous CUDA scratch tensor before
allocating the next one. While GPUWorker.load_model() applies
max_split_size_mb=20, this leaves fully free 20 MiB allocator segments that
cannot satisfy the next smaller upload. CUDA reserved memory therefore grows
while allocated memory remains flat, which can exhaust lower-memory GPUs or
livelock the loader in allocator retries.

This change chains the CPU narrow() view and .to(param.device) operation so
the previous CUDA tensor remains alive until the next allocation completes.
The tensor layout and destination copies are unchanged.

Thanks to @brocktice for documenting the allocator interaction and suggested
fix in this issue comment.

Duplicate check: on 2026-08-12, no open PR referenced #51205, and no open PR
matched the Inkling loader / max-split allocator keywords.

AI assistance disclosure: OpenAI Codex assisted with root-cause validation,
test implementation, H200 A/B execution, and PR drafting. The human submitter
reviewed every changed line and the attached evidence before submission.

Test Plan

CUDA_VISIBLE_DEVICES=7 .venv/bin/python -m pytest \
  tests/models/inkling/test_moe_weight_layout.py::test_moe_w13_upload_reuses_cuda_allocator_segments \
  -v -s

CUDA_VISIBLE_DEVICES=7 .venv/bin/python -m pytest \
  tests/models/inkling/test_moe_weight_layout.py -v

.venv/bin/pre-commit run --files \
  vllm/models/inkling/nvidia/moe.py \
  tests/models/inkling/test_moe_weight_layout.py

Full-model validation used thinkingmachines/Inkling-Small-NVFP4 revision
b6a99534467840620d411e4cd4ad5819b2610d9c on 2x H200 with TP2, a 200k maximum
model length, chunked prefill, prefix caching, and the Inkling parsers. The
controlled allocator A/B removed the expandable_segments override so vLLM's
20 MiB load scope governed allocations, and used --enforce-eager to avoid an
independent upstream Inkling CUDA Graph assertion.

Test Result

• Regression test on the upstream loader: expected failure with
  335544320 bytes of scratch CUDA memory reserved; 1 failed in 10.11s.
• Regression test with this patch: 1 passed, 14 warnings in 9.43s.
• Nearby Inkling suite on the final rebased branch:
  34 passed, 14 warnings in 17.04s.
• All applicable pre-commit hooks passed.

At the final instrumented w13 upload on each TP rank:

Measurement	Upstream loader	Patched loader
Allocated memory	78.3110 GiB	78.3110 GiB
Reserved memory	137.2695 GiB	80.5078 GiB
Reserved minus allocated	58.9586 GiB	2.1968 GiB
Loaded checkpoint shards	10/10	10/10
API ready	Yes	Yes
Eager 8-token generation	Passed	Passed

The patch reduced reserved-minus-allocated overhead by 56.7617 GiB per rank,
or 96.27%. This is a memory validation, not a load-time performance claim; the
sequential runs shared filesystem and kernel caches.

With the issue's original PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True,
PyTorch 2.13.0+cu132 kept reserved memory flat and loaded all shards, so that
environment masks the allocator interaction on this machine. Its generation
request then hit a separate upstream CUDA Graph input-address assertion. The
controlled eager A/B above isolates the loader behavior.

The complete CUDA test requirements install was attempted, but the unrelated
optional arctic-inference==0.1.1 C++ build failed because <span> was not
available. The directly required test dependency was installed, and the target
suite collected and passed as reported above.

---

• The purpose and linked issue are documented.
• Test commands are provided.
• RED/GREEN and full-model results are provided.
• No documentation update is needed for this loader bug fix.

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