NVIDIA DGX Spark 2-Node Linked Cluster

Overview

Two Sparks, linked — and the model class roughly doubles. NVIDIA supports connecting a pair of DGX Spark units over their built-in ConnectX-7 200GbE interfaces, combining 256 GB of unified memory to reach approximately the 405B-parameter class. Still desk-side, still on a normal wall socket, still entirely inside your building.

Key highlights

• 2× NVIDIA GB10 Grace Blackwell Superchips — ~2 petaFLOPS FP4 combined.
• 256 GB unified LPDDR5x across the pair — CPU and GPU share the pool on each node.
• ~405B-parameter class — a size that normally demands rented cluster time.
• ConnectX-7 200GbE interconnect between units — the supported NVIDIA link path.
• 8 TB NVMe combined for datasets, checkpoints and model weights.
• Two 150 mm boxes on a desk — no rack, no facility cooling, no dedicated circuit.
• DGX OS and the full NVIDIA AI stack on both nodes, ready on power-up.

AI workload fit

• Local inference at the ~405B class.
• Fine-tuning substantially larger models than a single Spark allows.
• RAG over private corpora that must never leave the premises.
• Agentic AI development against frontier-scale open models.
• Generative AI, computer vision, NLP & speech prototyping.
• Teaching and demonstrating distributed inference on real hardware.

AI workload positioning

The cheapest honest route to frontier-scale open weights on-premises. Sweep-level arithmetic makes the point: the largest trending open models have outgrown a single node’s memory, and this pair is the smallest, quietest way to hold that class of model under your own roof. It is a capacity play, not a throughput one — for production concurrency, step to a GPU server.

Industry use cases

• Research & education: frontier-model experimentation without cluster allocation.
• BFSI: evaluate large open models against confidential data, on-premises.
• Healthcare: large clinical models on protected patient data under DPDP.
• Enterprise & GCCs: an AI platform team’s reference rig for model selection.
• Public sector & sovereign: sovereign model evaluation on department premises.

Performance & how to be sure

Linking two Sparks raises the ceiling on which model fits, not how fast it serves; interconnect over 200GbE is far below on-package bandwidth, so this is a development and evaluation platform rather than a production serving node. Rather than quote a tokens/sec figure that will not match your workload, RDP offers a “benchmark your model” session: bring the model, precision and context length you actually intend to run, and we will validate it on this exact configuration before you commit.

Series & upgrade path

CARINA entry tier. Below: a single DGX Spark (~200B class). Above: the QUASAR and DRACO aiDAPTIV+ workstations for local fine-tuning throughput, then RDP GPU servers (4×/8× H200, B200, B300) for production concurrency, then rack-scale. Same CUDA stack at every rung.

On-prem vs cloud (TCO)

For sustained daily AI work this configuration removes per-GPU-hour billing, queueing for scarce instances, and egress charges on your own data — and keeps everything on-premises for DPDP and data-residency obligations. Cloud still wins for burst capacity and one-off very large training runs; on-prem wins on sustained utilisation, control and predictable INR capital cost. We model the crossover with your actual usage rather than assert it.

Software & day-one readiness

Each unit ships with NVIDIA DGX OS and the full NVIDIA AI stack — CUDA, cuDNN, containers, and the common serving and fine-tuning frameworks (vLLM, PyTorch, NGC catalogue). It is the same CUDA environment as every larger NVIDIA system RDP supplies, which is why work developed here moves upward to a GPU server or rack-scale system without a rewrite.

Power, thermal & acoustics

Two compact desktop units, each on a standard 230V socket with an external adapter. Air-cooled, office-appropriate acoustics, no rack or facility work of any kind. Exact wattage, BTU and dB(A) figures come from RDP bench measurement rather than estimates — ask for the site-readiness sheet with your quote.

Deployment, warranty & support

Built to order by RDP Technologies. Supplied with GST invoice (HSN 8471), pan-India onsite support, and availability through GeM for public-sector procurement. Built to order — typically 4–6 weeks, subject to allocation.

Why RDP

RDP Technologies is a Make-in-India OEM with 14+ years and 300,000+ units shipped, supplying AI infrastructure from desk-side systems to rack-scale AI factories — with predictable INR pricing, GST invoicing and pan-India onsite service.

Buy with confidence

Use Request a Quote to reach an RDP solution architect for sizing, a benchmark-your-model session, financing options and a delivery plan. No obligation.

Specifications