Unused vector name cost storage and (maybe)memory

Hello, i have a big collection which info shows below:

{
  "result": {
    "status": "yellow",
    "optimizer_status": "ok",
    "vectors_count": 29225918,
    "indexed_vectors_count": 19271275,
    "points_count": 23517920,
    "segments_count": 10,
    "config": {
      "params": {
        "vectors": {
          "gemini:models/embedding-001": {
            "size": 768,
            "distance": "Cosine"
          },
          "openai:text-embedding-ada-002": {
            "size": 1536,
            "distance": "Cosine",
            "on_disk": true
          }
        },
        "shard_number": 1,
        "replication_factor": 1,
        "write_consistency_factor": 1,
        "on_disk_payload": true
      },
      "hnsw_config": {
        "m": 16,
        "ef_construct": 100,
        "full_scan_threshold": 10000,
        "max_indexing_threads": 0,
        "on_disk": false
      },
      "optimizer_config": {
        "deleted_threshold": 0.2,
        "vacuum_min_vector_number": 1000,
        "default_segment_number": 0,
        "max_segment_size": null,
        "memmap_threshold": 120000,
        "indexing_threshold": 20000,
        "flush_interval_sec": 5,
        "max_optimization_threads": 16
      },
      "wal_config": {
        "wal_capacity_mb": 32,
        "wal_segments_ahead": 0
      },
      "quantization_config": {
        "binary": {
          "always_ram": true
        }
      }
    },
    "payload_schema": {
      "content": {
        "data_type": "text",
        "points": 23513232
      },
      "created_at": {
        "data_type": "integer",
        "points": 23513232
      }
    }
  },
  "status": "ok",
  "time": 0.000123933
}

I have 2 vectors setting, "openai:text-embedding-ada-002" which is in current using, and "gemini:models/embedding-001" is design for future usage and i did not put any vectors in it.
what confusing me is that, even in current config which optimize for memory usage(vector&payload on_disk), this collection still consume over 180GB memory, dose the payload index costs? or empty vectors has memory costs? I'll appreciate it if you guys can give some guides cuz i cannot find docs about it by myself.
And another thing i found out is that unused vector name costs disk storage:

find .  -name '*gemini*'|xargs du -sh -c
1.5G	./e5dd2e49-63ba-453e-a8c5-c1660205a05e/vector_index-gemini:models
8.6G	./e5dd2e49-63ba-453e-a8c5-c1660205a05e/vector_storage-gemini:models
332M	./00f91f9c-82e1-42e9-b27e-6b3f85121984/vector_index-gemini:models
5.6G	./00f91f9c-82e1-42e9-b27e-6b3f85121984/vector_storage-gemini:models
2.3G	./0d0d5bac-68f7-4f44-8067-b5293b6d89f0/vector_index-gemini:models
5.7G	./0d0d5bac-68f7-4f44-8067-b5293b6d89f0/vector_storage-gemini:models
2.1G	./7fce8419-baf2-4a4d-8b74-9ddf16243ec5/vector_index-gemini:models
3.0G	./7fce8419-baf2-4a4d-8b74-9ddf16243ec5/vector_storage-gemini:models
627M	./2783ed36-f8cf-4f81-8d4f-3b9ee58c091d/vector_index-gemini:models
5.2G	./2783ed36-f8cf-4f81-8d4f-3b9ee58c091d/vector_storage-gemini:models
217M	./a3d5f780-faed-439e-946d-f0adcd049423/vector_index-gemini:models
5.6G	./a3d5f780-faed-439e-946d-f0adcd049423/vector_storage-gemini:models
170M	./0536f81d-c847-47bf-86fc-4d145dbdda67/vector_index-gemini:models
4.7G	./0536f81d-c847-47bf-86fc-4d145dbdda67/vector_storage-gemini:models
46G	total

find .  -name '*ada-002*'|xargs du -sh -c
3.2G	./e5dd2e49-63ba-453e-a8c5-c1660205a05e/vector_index-openai:text-embedding-ada-002
18G	./e5dd2e49-63ba-453e-a8c5-c1660205a05e/vector_storage-openai:text-embedding-ada-002
3.0G	./00f91f9c-82e1-42e9-b27e-6b3f85121984/vector_index-openai:text-embedding-ada-002
12G	./00f91f9c-82e1-42e9-b27e-6b3f85121984/vector_storage-openai:text-embedding-ada-002
2.7G	./0d0d5bac-68f7-4f44-8067-b5293b6d89f0/vector_index-openai:text-embedding-ada-002
12G	./0d0d5bac-68f7-4f44-8067-b5293b6d89f0/vector_storage-openai:text-embedding-ada-002
1.4G	./7fce8419-baf2-4a4d-8b74-9ddf16243ec5/vector_index-openai:text-embedding-ada-002
5.9G	./7fce8419-baf2-4a4d-8b74-9ddf16243ec5/vector_storage-openai:text-embedding-ada-002
2.0G	./2783ed36-f8cf-4f81-8d4f-3b9ee58c091d/vector_index-openai:text-embedding-ada-002
11G	./2783ed36-f8cf-4f81-8d4f-3b9ee58c091d/vector_storage-openai:text-embedding-ada-002
3.6G	./a3d5f780-faed-439e-946d-f0adcd049423/vector_index-openai:text-embedding-ada-002
12G	./a3d5f780-faed-439e-946d-f0adcd049423/vector_storage-openai:text-embedding-ada-002
6.2M	./68edd3d7-01a6-4835-a406-af7e3e38689d/vector_storage-openai:text-embedding-ada-002
57G	./f49fb9ac-e287-4e02-b37a-0684f45a9796/vector_storage-openai:text-embedding-ada-002
3.4G	./0536f81d-c847-47bf-86fc-4d145dbdda67/vector_index-openai:text-embedding-ada-002
9.3G	./0536f81d-c847-47bf-86fc-4d145dbdda67/vector_storage-openai:text-embedding-ada-002
34M	./8ceadfdd-f6ed-4710-a39d-3c98e601e579/vector_storage-openai:text-embedding-ada-002
8.2G	./1fe48489-32da-49b2-9230-a931d378d374/vector_storage-openai:text-embedding-ada-002
161G	total

Dose it the normal case? or some thing wrong already happened on the collection causes it?
PS: currently i'm using qdrant version 1.8.3
Thx!

No source file, test, or entry point is named. Start by reproducing the reported memory and disk usage with Qdrant 1.8.3 and the two configured vector names, then trace how unused vector storage and payload indexes are allocated; done means documenting the expected behavior or isolating a reproducible defect.