NVIDIA adds official eight-node DGX Spark clusters, reaching 1 TB unified memory
At GTC Berlin, NVIDIA announced official support for eight-node DGX Spark clusters, which scale unified memory from 128 GB on a single node to 512 GB on four nodes and 1 TB on eight, per the company's technical blog. NVIDIA says the capacity lets the full 753B GLM 5.3 checkpoint from Z.ai run across the 8x 128 GB cluster, and it measured roughly 80 tokens per second generating with GLM 5.3 Flash. Vendor benchmarks also show dense Qwen3.8-27B decode rising from about 12 TPS on one node to 52 TPS on eight, and nanochat pre-training delivering a 7.79x speedup at 97.4% scaling efficiency; NVIDIA says eight-node support in NVIDIA Sync, whose Cluster Assistant handles setup and device management, is coming by the end of the year.
NVIDIA's figures are vendor claims from a single blog post, but they position a desk-side Spark cluster as able to serve large open models and train small ones locally.
AI developers aren’t just running a single model anymore—they’re orchestrating multiple models in parallel, chaining inference calls, and experimenting with frontier-class architectures that demand hundreds of gigabytes of memory. NVIDIA DGX Spark was built to bring AI development to the desk, and now you’ll be able to link even more Spark nodes together.
At GTC Berlin, NVIDIA announced official support for eight-node DGX Spark clusters, scaling unified memory to 1 TB and opening the door to frontier-class models, multi-agent workloads, and near-linear pre-training—all running locally.
NVIDIA Sync makes it easy to cluster. Its Cluster Assistant handles setup and device management. Support for eight-node clusters in NVIDIA Sync is coming by the end of the year.
Read on for benchmark data on how inference, orchestration, and training scale as the cluster grows.
Run larger, newer frontier-class models
The biggest benefit of an eight-node cluster is memory. A single DGX Spark provides 128 GB of unified memory; four nodes reach 512 GB; eight nodes reach 1 TB. That capacity opens the door to a class of models that previously wouldn’t start on a four-node system, including the full GLM 5.3 (753B) model from Z.ai.
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