Performance Tip: Lock Local Model VRAM/RAM with LimitMEMLOCK=infinity

If your local model fits inside your combined VRAM and RAM, you can keep the model provider's memory resident by adding LimitMEMLOCK=infinity under the [Service] header of the systemd unit for your provider. The kernel then stops paging model weights out to disk, which is where most of the slowdowns come from once a model is loaded.

The unit file

Posted in r/openclaw as a working example for LM Studio (edit the paths and usernames to match your install):

[Unit]
Description=LM Studio Server
RequiresMountsFor=/home

[Service]
LimitMEMLOCK=infinity
Type=oneshot
RemainAfterExit=yes
User=********
Environment="HOME=/home/"
ExecStartPre=/home//.lmstudio/bin/lms daemon up
ExecStartPre=/home//.lmstudio/bin/lms load text-embedding-bge-large-en-v1.5 --yes
ExecStartPre=/home//.lmstudio/bin/lms load qwen3.6-35b-a3b-uncensored-hauhaucs-aggressive --yes
ExecStart=/home//.lmstudio/bin/lms server start
ExecStop=/home//.lmstudio/bin/lms daemon down
[Install]
WantedBy=multi-user.target

What each part does

• LimitMEMLOCK=infinity — removes the default per-process mlock cap, so the loaded model can be kept out of swap/paging.
• ExecStartPre with lms daemon up — starts the LM Studio daemon before anything else.
• lms load <model> --yes — preloads each model at service start. The example loads an embedding model (text-embedding-bge-large-en-v1.5) and a chat model (qwen3.6-35b-a3b-uncensored-hauhaucs-aggressive).
• lms server start as ExecStart — brings up the OpenAI-compatible server.
• lms daemon down as ExecStop — cleans up on shutdown.
• RequiresMountsFor=/home — ensures the home partition holding the model files is mounted before the service starts.
• Type=oneshot + RemainAfterExit=yes — the unit is treated as active after the start commands finish, which fits a daemon the lms CLI manages separately.

Caveat

The tip explicitly assumes the model fits in VRAM+RAM. If it doesn't, locking memory is the wrong lever — you'll just push the problem elsewhere. Once it fits, the win is avoiding paging-induced stalls on inference.

To apply: drop the block into your unit file, then systemctl daemon-reload and restart the service. Developers running LM Studio headless as a systemd service are the target audience here; the same idea applies to any provider process whose weights you want pinned in memory.