Running Frontier-Class AI Models Across 8 NVIDIA DGX Spark Nodes

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. Beyond simply fitting these models, the cluster is designed to serve them interactively. As an early example, we ran GLM 5.3 Flash across an eight-node cluster, and measured roughly 80 tokens per second (TPS) in generation, suitable for interactive local development against a frontier-class mixture-of-experts model. The full GLM 5.3 (753B) checkpoint runs across the 8x 128 GB cluster, making it one of the largest open models you can serve locally. ConcurrencyPrefill, total tokens per second (TPS)Prefill, per user (TPS)Decode, total (TPS)Decode, per user (TPS)11,6001,600313141,600750512181,6004736718Table 1. Full GLM 5.3 (nvidia/GLM-5.3-NVFP4) performance on an eight-node DGX Spark cluster. The model is about 470 GB in NVFP4, measured with MTP3 at concurrency one, four, and eight. Prefill measured at 8K input sequence length (ISL) per request. Faster collectives with a Spark-optimized backend Tensor-parallel inference spends a meaningful fraction of its time in collective operations such as all-reduce. On switch-connected clusters, we have been evaluating a Spark-optimized collective backend, RoCEnante, that reduces collective latency for the small-message, low-concurrency, modest-rank-count pattern typical of tensor-parallel decode on local AI …