---
name: ascend-memory-profiling
description: Attribute vLLM serving HBM usage on Ascend to weights, KV cache, HCCL, activations and runtime using measured evidence. Use for 显存归因, 显存 profiling, or 内存各部分拆分. A quick current memory-usage or idle-card lookup uses the fleet monitor; kernel latency analysis uses profiling-analysis.
---

# ascend-memory-profiling

Attribute serving HBM to fixed overhead, weights, KV cache, HCCL, activations and runtime with explicit evidence.

Prefer measured msprof and npu-smi evidence, then startup logs and tensor headers. Header byte sizes are exact; per-device sharding and component labels may be inferred. Model-config estimates are a fallback. Keep residual memory visible instead of forcing categories to balance.

## Agent entry

Run from the repository root using the platform's Python launcher. The workspace
selects its installed platform environment automatically.

```text
python .agents/skills/ascend-memory-profiling/scripts/mem_collect.py --help
```

mem_collect.py accepts the serving workload and captures evidence through the managed service. mem_analyze.py consumes the collection output. Keep collection parameters tied to the user question; scripts own lifecycle and report generation.

Use profiling analysis for kernel timing, bubbles and communication latency.

Read the relevant detail only when needed:

- [methodology](references/methodology.md)
- [msprof_fields](references/msprof_fields.md)