[BUG] Machine learning memory leak
### The bug

yacht dashboar is showing that immich_machine_learning is consuming between 20%-60% of ram

### The OS that Immich Server is running on

Debian

### Version of Immich Server

v1.66.1

### Version of Immich Mobile App

v1.66.0

### Platform with the issue

- [X] Server
- [ ] Web
- [ ] Mobile

### Your docker-compose.yml content

```YAML
version: "3.8"

services:
  immich-server:
    container_name: immich_server
    image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
    command: [ "start.sh", "immich" ]
    volumes:
      - ${UPLOAD_LOCATION}:/usr/src/app/upload
    env_file:
      - .env
    depends_on:
      - redis
      - database
      - typesense
    restart: always

  immich-microservices:
    container_name: immich_microservices
    image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
    command: [ "start.sh", "microservices" ]
    volumes:
      - ${UPLOAD_LOCATION}:/usr/src/app/upload
    env_file:
      - .env
    depends_on:
      - redis
      - database
      - typesense
    restart: always

  immich-machine-learning:
    container_name: immich_machine_learning
    image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
    volumes:
      - model-cache:/cache
    env_file:
      - .env
    restart: always

  immich-web:
    container_name: immich_web
    image: ghcr.io/immich-app/immich-web:${IMMICH_VERSION:-release}
    env_file:
      - .env
    restart: always

  typesense:
    container_name: immich_typesense
    image: typesense/typesense:0.24.1@sha256:9bcff2b829f12074426ca044b56160ca9d777a0c488303469143dd9f8259d4dd
    environment:
      - TYPESENSE_API_KEY=${TYPESENSE_API_KEY}
      - TYPESENSE_DATA_DIR=/data
    logging:
      driver: none
    volumes:
      - tsdata:/data
    restart: always

  redis:
    container_name: immich_redis
    image: redis:6.2-alpine@sha256:70a7a5b641117670beae0d80658430853896b5ef269ccf00d1827427e3263fa3
    restart: always

  database:
    container_name: immich_postgres
    image: postgres:14-alpine@sha256:28407a9961e76f2d285dc6991e8e48893503cc3836a4755bbc2d40bcc272a441
    env_file:
      - .env
    environment:
      POSTGRES_PASSWORD: ${DB_PASSWORD}
      POSTGRES_USER: ${DB_USERNAME}
      POSTGRES_DB: ${DB_DATABASE_NAME}
      PG_DATA: /var/lib/postgresql/data
    volumes:
      - pgdata:/var/lib/postgresql/data
    restart: always

  immich-proxy:
    container_name: immich_proxy
    image: ghcr.io/immich-app/immich-proxy:${IMMICH_VERSION:-release}
    environment:
      # Make sure these values get passed through from the env file
      - IMMICH_SERVER_URL
      - IMMICH_WEB_URL
    ports:
      - 2283:8080
    depends_on:
      - immich-server
      - immich-web
    restart: always

volumes:
  pgdata:
  model-cache:
  tsdata:
```


### Your .env content

```Shell
###################################################################################
# Database
###################################################################################

DB_HOSTNAME=immich_postgres
DB_USERNAME=postgres
DB_PASSWORD=postgres
DB_DATABASE_NAME=immich

# Optional Database settings:
# DB_PORT=5432

###################################################################################
# Redis
###################################################################################

REDIS_HOSTNAME=immich_redis

# Optional Redis settings:

# Note: these parameters are not automatically passed to the Redis Container
# to do so, please edit the docker-compose.yml file as well. Redis is not configured
# via environment variables, only redis.conf or the command line

# REDIS_PORT=6379
# REDIS_DBINDEX=0
# REDIS_PASSWORD=
# REDIS_SOCKET=

###################################################################################
# Upload File Location
#
# This is the location where uploaded files are stored.
###################################################################################

UPLOAD_LOCATION=/srv/dev-disk-by-uuid-73d1767c-8c6f-4fd0-bf8b-da093241cda3/Backup/Immich/


###################################################################################
# Typesense
###################################################################################
TYPESENSE_API_KEY=blablablahq1q1q1!!!
# TYPESENSE_ENABLED=false

###################################################################################
# Reverse Geocoding
#
# Reverse geocoding is done locally which has a small impact on memory usage
# This memory usage can be altered by changing the REVERSE_GEOCODING_PRECISION variable
# This ranges from 0-3 with 3 being the most precise
# 3 - Cities > 500 population: ~200MB RAM
# 2 - Cities > 1000 population: ~150MB RAM
# 1 - Cities > 5000 population: ~80MB RAM
# 0 - Cities > 15000 population: ~40MB RAM
####################################################################################

# DISABLE_REVERSE_GEOCODING=false
# REVERSE_GEOCODING_PRECISION=3

####################################################################################
# WEB - Optional
#
# Custom message on the login page, should be written in HTML form.
# For example:
# PUBLIC_LOGIN_PAGE_MESSAGE="This is a demo instance of Immich.<br><br>Email: <i>demo@demo.de</i><br>Password: <i>demo</i>"
####################################################################################

PUBLIC_LOGIN_PAGE_MESSAGE=Hello!

####################################################################################
# Alternative Service Addresses - Optional
#
# This is an advanced feature for users who may be running their immich services on different hosts.
# It will not change which address or port that services bind to within their containers, but it will change where other services look for their peers.
# Note: immich-microservices is bound to 3002, but no references are made
####################################################################################

IMMICH_WEB_URL=http://immich-web:3000
IMMICH_SERVER_URL=http://immich-server:3001
IMMICH_MACHINE_LEARNING_URL=http://immich-machine-learning:3003

####################################################################################
# Alternative API's External Address - Optional
#
# This is an advanced feature used to control the public server endpoint returned to clients during Well-known discovery.
# You should only use this if you want mobile apps to access the immich API over a custom URL. Do not include trailing slash.
# NOTE: At this time, the web app will not be affected by this setting and will continue to use the relative path: /api
# Examples: http://localhost:3001, http://immich-api.example.com, etc
####################################################################################

#IMMICH_API_URL_EXTERNAL=http://localhost:3001
```


### Reproduction steps

```bash
sudo docker-compose pull && sudo docker-compose up -d
```


### Additional information

_No response_
diff --git a/machine-learning/app/config.py b/machine-learning/app/config.py
index 70520b27..f5cb8359 100644
--- a/machine-learning/app/config.py
+++ b/machine-learning/app/config.py
@@ -13,7 +13,7 @@ class Settings(BaseSettings):
     facial_recognition_model: str = "buffalo_l"
     min_tag_score: float = 0.9
     eager_startup: bool = True
-    model_ttl: int = 300
+    model_ttl: int = 0
     host: str = "0.0.0.0"
     port: int = 3003
     workers: int = 1
diff --git a/machine-learning/app/main.py b/machine-learning/app/main.py
index 35ee2720..264eb2ee 100644
--- a/machine-learning/app/main.py
+++ b/machine-learning/app/main.py
@@ -25,7 +25,7 @@ app = FastAPI()
 
 
 def init_state() -> None:
-    app.state.model_cache = ModelCache(ttl=settings.model_ttl, revalidate=True)
+    app.state.model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
 
 
 async def load_models() -> None:
diff --git a/machine-learning/app/models/cache.py b/machine-learning/app/models/cache.py
index 086a57c5..b9d5f75a 100644
--- a/machine-learning/app/models/cache.py
+++ b/machine-learning/app/models/cache.py
@@ -1,4 +1,3 @@
-import asyncio
 from typing import Any
 
 from aiocache.backends.memory import SimpleMemoryCache
@@ -48,13 +47,10 @@ class ModelCache:
         """
 
         key = self.cache.build_key(model_name, model_type.value)
-        model = await self.cache.get(key)
-        if model is None:
-            async with OptimisticLock(self.cache, key) as lock:
-                model = await asyncio.get_running_loop().run_in_executor(
-                    None,
-                    lambda: InferenceModel.from_model_type(model_type, model_name, **model_kwargs),
-                )
+        async with OptimisticLock(self.cache, key) as lock:
+            model = await self.cache.get(key)
+            if model is None:
+                model = InferenceModel.from_model_type(model_type, model_name, **model_kwargs)
                 await lock.cas(model, ttl=self.ttl)
         return model
 
['machine-learning/app/main.py', 'machine-learning/app/models/cache.py', 'machine-learning/app/config.py']