[Core] Size the KV cache from this process's own device memory, not the device-wide delta

[twu3202]

Purpose

memory_profiling() derives total_consumed (weights + non-torch overhead, the base of the KV cache budget) from the
device-wide free-memory delta between the worker's init snapshot and the end of the profile run. Anything another
process allocates or releases on the same device in that window is charged to, or credited against, this instance:
two instances started at the same time on one GPU shrink each other's KV cache or fail with
No available memory for the cache blocks, and a release by another process trips the
init_free_memory >= free_gpu_memory assertion. Fixes #55827; #55169 hit the same mechanism with sleep/wake
model swapping (closed as interference from concurrent GPU use), #35920 is the UMA variant, and #56830 is the
assertion side of the same mechanism (a co-tenant releasing memory during profiling, or host memory moving on
a unified-memory part with no co-tenant at all).

Changes:

• Platform.get_process_memory_usage(device_id) returns the device memory used by the current process
  (None when the platform cannot attribute memory to processes); NvmlCudaPlatform implements it through
  nvmlDeviceGetComputeRunningProcesses and returns None for any NVML error, missing PID entry (e.g. a container
  whose PID namespace NVML does not see), WDDM/MIG (usedGpuMemory unavailable).
• MemorySnapshot records it as process_memory; memory_profiling() uses after − before of that value as
  total_consumed when both snapshots have it (MemoryProfilingResult.process_scoped = True) and logs a warning when
  the device-wide delta differs from it by ≥ 128 MiB (other processes changed their usage while we were loading).
  Without per-process data the behaviour is unchanged.
• Worker.determine_available_memory keeps the release-during-profiling assertion only for the device-level path;
  with per-process accounting a release by others is logged and ignored.

gpu_memory_utilization keeps its meaning (this instance uses at most that fraction of the device); the start-time
check that the requested memory is free is unchanged, so co-tenants that together request more than the device has
still fail at allocation time rather than silently.

Test Plan

• tests/utils_/test_mem_utils.py: mocked platform — another process allocating between the snapshots is not
  counted (process_scoped), fallback to the device delta when per-process usage is unavailable, snapshot
  subtraction; real device — a spawned helper process allocates 512 MiB while this process profiles, total_consumed
  must reflect only this process's 256 MiB (skipped when the platform cannot report per-process usage). Existing
  test_memory_profiling unchanged (own allocations are counted identically by both methods).
• End to end (repro from [Bug]: Starting two vLLM instances on one GPU at the same time makes each count the other's model loading as its own non-torch memory (KV cache shrinks or "No available memory for the cache blocks") #55827): Qwen/Qwen3-ASR-1.7B at util 0.35 and Qwen/Qwen3-ForcedAligner-0.6B at
  util 0.20 started sequentially (reference), concurrently, and the aligner 1 s after killing the ASR.

Test Result

• Unit tests: 7 passed (tests/utils_/test_mem_utils.py, RTX 6000 Ada, main + this patch as an overlay).

• End to end, RTX 6000 Ada 48 GB (bare metal, driver 595.84), main @ 8a0a7ee + this patch as an overlay,
  Qwen/Qwen3-ASR-1.7B at util 0.35 and Qwen/Qwen3-ForcedAligner-0.6B at util 0.20:

  start order	ASR KV before	aligner KV before	ASR KV after	aligner KV after
  sequential (reference)	10.82 GiB	6.50 GiB	10.82 GiB	6.50 GiB
  both at once	9.01 GiB	−0.01 GiB → No available memory for the cache blocks	10.82 GiB	6.50 GiB
  aligner started 1 s after killing the ASR	–	6.50 GiB	–	6.50 GiB, no assertion

  With the patch the concurrent start logs, e.g. for the ASR, Other processes on cuda:0 changed their device memory usage by 8.32 GiB while this instance was loading and profiling. The KV cache budget is based on this process's own usage (4.68 GiB) rather than the device-wide change (13.0 GiB).

• RTX 5090 32 GB (docker, driver 595.80; NVML reports the container PID), main @ 144e79c + this patch as an overlay,
  same models and utilisations:

  start order	ASR KV before	aligner KV before	ASR KV after	aligner KV after
  sequential (reference)	4.72 GiB	2.82 GiB	4.72 GiB	2.82 GiB
  both at once	2.50 GiB	−3.86 GiB → No available memory for the cache blocks	4.72 GiB	2.82 GiB
  aligner started 1 s after killing the ASR	–	2.82 GiB	–	2.82 GiB

  Unit tests: 7 passed there as well.

Developed with AI assistance (Claude); I reviewed every changed line and ran the tests above.

Independently verified on an integrated-GPU part (DGX Spark / GB10, unified memory) by hclsys — see this comment: host-only memory churn trips the unpatched assertion, and the patched KV budget is unchanged by it.

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[claude]

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[hclsys]

I have a DGX Spark (GB10, sm_121, aarch64, CUDA 13, driver 580.95.05) so I ran the NVML primitives this depends on against that device, since integrated/unified-memory parts are where per-process attribution is most likely to quietly not work.

It works there:

• platform resolves to NvmlCudaPlatform, is_integrated_gpu() is True, so get_process_memory_usage is the live path rather than the None fallback
• nvmlDeviceGetComputeRunningProcesses lists our own PID, and usedGpuMemory comes back as an int, not None
• it tracks this process only: allocating 512 MiB moved usedGpuMemory 741326848 -> 1278197760, i.e. exactly 0.500 GiB

One thing worth knowing for this file: nvmlDeviceGetMemoryInfo is NVMLError_NotSupported on GB10 (nvidia-smi --query-gpu=memory.total also returns [N/A], and get_device_total_memory right above your new method raises there). Your method does not call it, so it is unaffected — flagging it only so nobody later "simplifies" get_process_memory_usage to go through the device-level query.

Why I bothered: #56830 filed today calls out the integrated-GPU row specifically. On unified memory the entire host is effectively a co-tenant, so the init_free_memory >= free_gpu_memory assert can fire with vLLM as sole tenant and nothing sharing the GPU, and the message's advice to "isolate vLLM in its own container" cannot help. The #53591 fallback is gated on is_rocm(), so it does not reach that row. Per-process accounting does.

To be clear about scope: I did not run a full vllm serve with your patch applied. I verified the NVML behaviour the patch relies on, on the hardware in question. Happy to run the end-to-end startup on the Spark if that would be useful to you.

[twu3202]

Thanks for checking the NVML side on GB10 — that is exactly the class of device I could not test here, and the nvmlDeviceGetMemoryInfo → NotSupported point is worth keeping: 0f1b2a7 adds a docstring note that get_process_memory_usage must stay on the per-process query.

An end-to-end startup on the Spark would help. The two things worth looking at are the KV-cache size the worker reports with and without the patch (sole tenant, so they should match), and whether the "Other processes on … changed their device memory usage" warning fires when host memory moves during the profiling window — on unified memory that is #56830's row 3.

[hclsys]

Ran it end to end on the Spark. Your patch at 0f1b2a7 as an overlay on 1a522b6 (only the four vllm/ files; the tests were not part of the overlay), Qwen/Qwen3-0.6B, gpu_memory_utilization=0.20, max_model_len=2048, eager.

For the second question I wanted host memory to move without anything touching the GPU, so the churner is a plain Python process with no CUDA context at all: bytearray(8 GiB), hold ~1.5 s, free, repeat, for the whole startup.

	quiet	host churn (CPU only, no CUDA)
without patch	KV 211,376 tok	AssertionError: Error in memory profiling. Initial free memory 66.23 GiB, current free memory 67.62 GiB
with patch	KV 212,080 tok	KV 212,080 tok

So on this device the assertion is reachable by host allocation churn alone, and your patch is what makes the budget immune to it. The KV size is bit-identical across both patched runs, and the warning reports the perturbation instead of failing:

• quiet: changed their device memory usage by 0.46 GiB ... own usage (1.48 GiB) rather than the device-wide change (1.94 GiB)
• churn: changed their device memory usage by 1.66 GiB ... own usage (1.48 GiB) rather than the device-wide change (3.15 GiB)

Own usage stays 1.48 GiB in both; only the device-wide number moves. That is the unified-memory failure mode stated as plainly as I can get it.

Quiet unpatched vs patched is 211,376 vs 212,080, +0.33%. I would not read much into the sign — the device-wide delta on this box also picks up whatever the host did during load.

Two caveats so the numbers are not over-read. This box is not a strict sole tenant: a long-running ASR server of mine holds ~16 GiB on the GPU throughout, steady, and present in all four runs — the churner is the only thing I varied. And all four runs die later at flashinfer.autotuner.set_autotune_process_group (a local flashinfer version gap, 4 occurrences in every log, well after the profiling phase), so the comparison is apples to apples but it is startup-to-KV-sizing, not a served request.

Happy to rerun at a larger model or a different utilisation if a specific configuration would be more convincing.

[twu3202]

Thank you for taking this all the way to an end-to-end run, and for building the churner. If I'm reading the table right, host-only churn is enough to trip the assertion on the unpatched build, while the patched budget comes out the same with and without it, which is what the process-scoped path is meant to buy on a unified-memory part.

On the 0.46 GiB in your quiet run, my guess is it's the same effect at rest: during model load the host's own activity shows up in the device-wide number, which is why the warning reports the device-wide vs own-usage split rather than trying to explain it. I don't think a larger model is needed — this is the configuration that matters. I've linked your run from the PR description.