NVIDIA DGX Spark 4-Seat AI Team Pod

Overview

Give four AI engineers their own supercomputer instead of four sets of cloud credits. Four DGX Spark units supplied, imaged and supported as one deployment: 512 GB of unified memory in total, each seat independently running models up to roughly the 200B class, all on-premises and all under your control.

Key highlights

• 4× NVIDIA DGX Spark — one per developer, each a complete AI system.
• 512 GB unified memory in total (128 GB per seat), ~200B-parameter class per unit.
• 16 TB NVMe across the pod for datasets and checkpoints.
• Deployed as one project: common image, common tooling, single support contract.
• Any two units can be linked over ConnectX-7 for ~405B-class work when needed.
• Four desks, four wall sockets — no rack, no server room, no facility spend.
• Replaces contended cloud GPU credits with fixed, predictable INR capital cost.

AI workload fit

• Per-developer inference and model experimentation.
• Individual fine-tuning without queueing for shared resources.
• RAG development against private data on each seat.
• Agentic AI and application development at team scale.
• Onboarding and training new AI engineers on real hardware.
• Pairing two seats for larger evaluation runs when required.

AI workload positioning

⚠️ Read this honestly: this is four independent machines, not one 512 GB memory domain. The unified-memory figure is the pod total, and a single model cannot span all four units. What it buys is parallel human productivity — four engineers each working uninterrupted on their own hardware — which for most teams is the real bottleneck, not peak throughput. If you need one large shared model, buy a GPU server instead and we will tell you so.

Industry use cases

• Enterprise & GCCs: equip an applied-AI or platform team with per-seat compute.
• Research & education: a teaching lab or research group with dedicated per-user systems.
• BFSI: a quant or data-science pod working on confidential data on-premises.
• Healthcare: clinical-informatics teams working within residency boundaries.
• Media & entertainment: per-artist generative workstations for image and video.

Performance & how to be sure

The metric that matters here is engineer-hours unblocked, not tokens per second: four people developing in parallel on dedicated hardware, with no queue and no per-hour meter. 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. Related: the DGX Spark 2-Node Linked Cluster (~405B on a single problem). Above: QUASAR/DRACO aiDAPTIV+ workstations for heavier per-seat fine-tuning, and RDP GPU servers when the team needs one large shared model rather than four independent ones.

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

Four compact desktop units on ordinary 230V office sockets. Air-cooled, quiet enough for open-plan desks, zero facility impact. 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