Connect two DGX Spark systems with a compatible QSFP cable through their ConnectX-7 ports, then configure the network with NVIDIA Sync Cluster Assistant or NVIDIA’s manual connection playbook. This enables distributed workloads across the systems; it does not create one shared 128 GB or 256 GB memory pool. Each Spark has its own 128 GB of unified system memory.
What connecting two DGX Spark systems does—and does not do
Each DGX Spark has 128 GB of LPDDR5x unified system memory, shared by that device’s CPU and GPU. NVIDIA says a dual-Spark configuration can support models up to 405B parameters, but this is a vendor capability statement about multi-node use—not a guarantee for every model or workload. Connecting the devices does not combine their memory into one transparent address space: software must distribute work across the nodes. NVIDIA’s DGX Spark hardware overview
What cable and link speed do you need?
Use Ethernet over the external ConnectX-7 QSFP ports. NVIDIA lists each port at up to 200 Gb/s; a cable rated for a higher speed does not raise the port’s limit. These ports are not an ordinary RJ-45 Ethernet connection.
NVIDIA lists these approved direct-attach copper cable models:
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Check the exact model, QSFP112 connector, and cable length before purchasing. Consult NVIDIA’s ConnectX-7 networking guide for current cabling details.
Choose a network setup method
| Option | What it handles | What you still need to do |
|---|---|---|
| NVIDIA Sync Cluster Assistant | Guided device discovery and network configuration, link-performance checks, and SSH setup. | Configure and launch the distributed workload separately. |
| Manual configuration | Network setup using NVIDIA’s connection playbook and Linux interface mapping. | Apply the right settings to the intended interfaces, then configure the workload separately. |
Sync’s Cluster Assistant supports up to three systems connected directly or up to four through a switch, according to NVIDIA’s documentation. For a two-system direct connection, Sync’s instructions call for exactly one QSFP cable between the devices. See NVIDIA’s Cluster Assistant documentation.
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Connect and configure the systems
- Prepare both systems. Start with two configured DGX Spark systems and choose a direct connection or a documented switch-based topology.
- Attach the QSFP cable. For a direct two-system setup managed by Sync, connect one approved cable between their ConnectX-7 QSFP ports. Make sure each end is fully seated.
- Configure the network. In NVIDIA Sync, add the systems and run Cluster Assistant to validate the devices, apply ConnectX-7 settings, check link performance, and configure SSH. For a manual setup, follow NVIDIA’s “Connect Two Sparks” playbook and the interface correspondence table in the networking guide.
- Check topology if setup fails. Confirm that the cables are seated and match the topology selected in Sync. NVIDIA also documents rebooting the systems with the cables attached as a troubleshooting step.
- Configure the distributed application. Choose and set up the software for the task—such as NCCL, vLLM, MPI, or a fine-tuning workflow. A working network alone does not distribute a workload.
Why do QSFP ports show multiple Linux interfaces?
Each QSFP port can appear as two Linux Ethernet interfaces because of the ConnectX-7 NIC’s PCIe topology. With two connected cables, Linux may show four interfaces. NVIDIA’s guide distinguishes Ethernet and RoCE interface names, so use its mapping and the connection playbook to select the intended interface instead of guessing based on names.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do you need a separate distributed-inference setup?
Yes. Network configuration and workload configuration are separate tasks. As one version-specific example, NVIDIA’s NIM for LLMs 1.15.0 DGX Spark deployment guide describes two-node distributed inference using ConnectX-7 and RoCE. Its instructions are tied to that documented version and software context; check the deployment guide for the models and software release you intend to run.
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