LM Studio on DGX Spark

30 MIN

Deploy LM Studio and serve LLMs on a Spark device; use LM Link to access models remotely.

SymptomCauseFix
API returns "model not found" errorModel not downloaded or loaded in LM StudioRun lms ls to verify download status, then load model with lms load {model-name}
lms command not foundPATH issue assuming successful installationRefresh your shell by running source ~/.bashrc
Model load fails - CUDA out of memoryModel too large for available VRAMSwitch to a smaller model or a different quantization
LM Link: devices not connecting or remote models not visibleDevices not in same Link, or LM Link not set up on bothEnsure both Spark and laptop are signed in and joined to the same Link at lmstudio.ai/link. Restart LM Studio/llmster after joining. See LM Link for how it works.

NOTE

DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. With many applications still updating to take advantage of UMA, you may encounter memory issues even when within the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:

sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'

For the latest known issues, please review the DGX Spark User Guide.