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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Yes—two DGX Spark systems can run some models that exceed one system’s capacity, but only when software distributes the model and its computation across both. NVIDIA lists support for models up to 405 billion parameters in a dual-Spark configuration and documents a two-system vLLM inference setup using tensor parallelism. That figure is a vendor capability claim, not a guarantee for every model, precision, context length, or runtime.
How two DGX Sparks can run a larger model
A single DGX Spark has 128 GB of unified system memory. NVIDIA lists capacity for models up to 200 billion parameters on one Spark and up to 405 billion parameters with two Sparks. These are NVIDIA’s stated capabilities; the documentation does not establish that every model of those sizes will fit at a particular precision or context length. NVIDIA DGX Spark hardware documentation
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Two systems do not automatically become one larger computer with pooled memory. They exchange data over a network, while a distributed framework partitions model work and state across them. NVIDIA’s vLLM playbook documents tensor parallelism across two Sparks for inference. It also distinguishes the two-Spark recipe from the single-Spark recipe, so do not assume single-system settings will work unchanged across both machines. NVIDIA vLLM playbook
What to confirm before choosing a model
- A maintained distributed recipe: Check that the exact model and intended framework have a two-node configuration. A model’s parameter count alone does not confirm it can be served on two Sparks.
- Memory requirements: Check the chosen quantization and context length as well as model size. NVIDIA’s 405B figure does not specify a universal precision, context length, or throughput.
- Software and launch settings: Confirm compatible versions and use the multi-node recipe’s container, parallelism, memory, and launch settings. The example configuration is not a blanket guarantee for other models.
- Network topology and speed: Plan direct cabling or a switch, and verify the link speed and configuration required by the selected setup.
How to connect two DGX Spark systems
Use an Ethernet-mode QSFP cable
For a direct connection, NVIDIA specifies Ethernet-mode QSFP cabling through the systems’ ConnectX-7 ports. Each port supports up to 200 Gb/s; a cable rated above that does not raise the port’s link speed. NVIDIA lists Amphenol NJAAKK-N911 and Luxshare LMTQF022-SD-R as approved cable options. NVIDIA multi-Spark hardware and setup guide
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A direct setup also involves interface and IP configuration plus inter-device SSH. NVIDIA’s connection playbook describes manual and automated setup steps. NVIDIA Connect Two Sparks playbook
Use Cluster Assistant for supported configurations
NVIDIA Sync’s Cluster Assistant supports two-to-four Spark/GB10 systems. It checks supported hardware, software minimums, SSH access, cables, link speed, and permissions, then configures networking and inter-device SSH. All nodes must run the April 2026 system software release or later. The assistant points users to workload playbooks, but does not install an arbitrary distributed model runtime or configure Slurm or Kubernetes. NVIDIA Sync Cluster Assistant documentation
Two- and three-system clusters can use direct cabling or a switch; four-system configurations require a switch. The assistant checks for at least 184 Gbit/s link speed. NVIDIA says a failed speed check can be investigated or bypassed at the user’s discretion; bypassing it does not establish that a workload will perform as intended.
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PAIR routes requests; it does not split a model
NVIDIA PAIR can route each request to a system that already has the requested model. It is not a way to combine memory for one oversized model: NVIDIA states, “PAIR sends each request to one system. It does not combine GPU memory, join GPUs into one larger GPU, or split a model or request across systems.” For distributed inference, use a workload recipe such as the documented multi-node vLLM path instead. NVIDIA PAIR overview
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Keep software current when troubleshooting
NVIDIA’s release notes report a February 2026 fix for a performance regression affecting some users with multiple Sparks connected after DGX OS 7.4.0. When diagnosing multi-node performance, check both systems’ software versions and consult the release notes. This note does not establish a performance level for any particular model or setup. NVIDIA DGX Spark release notes
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