# NVIDIA DGX Spark

NVIDIA announced at GTC Berlin official support for eight-node DGX Spark clusters, raising unified memory from 128 GB on a single node to 1 TB and letting frontier-class models that would not start on a four-node system load locally. The full GLM 5.3 (753B) checkpoint runs on the eight-node cluster, GLM 5.3 Flash generates at roughly 80 tokens per second, and decode throughput on dense Qwen3.8-27B (NVFP4) rises from about 12 TPS on one node to 52 TPS on eight.

NVIDIA announced at GTC Berlin official support for eight-node DGX Spark clusters, raising unified memory from 128 GB on a single node to 1 TB and letting frontier-class models that would not start on a four-node system load locally. The full GLM 5.3 (753B) checkpoint runs on the eight-node cluster, GLM 5.3 Flash generates at roughly 80 tokens per second, and decode throughput on dense Qwen3.8-27B (NVFP4) rises from about 12 TPS on one node to 52 TPS on eight.

At GTC Berlin, Nvidia laid out a local AI hardware line running from Windows PCs to desk-side workstations, and said DGX Spark can be manually configured into clusters of up to eight units with 1TB of unified memory, enough to run frontier-class models such as GLM 5.3. Previously, local devices were bounded by single-machine memory and ran smaller models or leaned on the cloud. A companion NVIDIA Sync Model Launcher reduces model download, container setup and OpenCode connection to a few clicks.

At GTC Berlin, Nvidia laid out a local AI hardware line running from Windows PCs to desk-side workstations, and said DGX Spark can be manually configured into clusters of up to eight units with 1TB of unified memory, enough to run frontier-class models such as GLM 5.3. Previously, local devices were bounded by single-machine memory and ran smaller models or leaned on the cloud. A companion NVIDIA Sync Model Launcher reduces model download, container setup and OpenCode connection to a few clicks.