{"id":12701,"date":"2026-08-18T22:20:31","date_gmt":"2026-08-18T22:20:31","guid":{"rendered":"https:\/\/rdp.in\/gpu-mart\/product\/nvidia-dgx-spark-4-seat-ai-team-pod\/"},"modified":"2026-08-18T23:24:42","modified_gmt":"2026-08-18T23:24:42","slug":"nvidia-dgx-spark-4-seat-ai-team-pod","status":"publish","type":"product","link":"https:\/\/rdp.in\/gpu-mart\/product\/nvidia-dgx-spark-4-seat-ai-team-pod\/","title":{"rendered":"NVIDIA DGX Spark 4-Seat AI Team Pod"},"content":{"rendered":"

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

Key highlights<\/h3>\n
    \n
  • 4\u00d7 NVIDIA DGX Spark<\/strong> \u2014 one per developer, each a complete AI system.<\/li>\n
  • 512 GB unified memory in total<\/strong> (128 GB per seat), ~200B-parameter class per unit.<\/li>\n
  • 16 TB NVMe<\/strong> across the pod for datasets and checkpoints.<\/li>\n
  • Deployed as one project<\/strong>: common image, common tooling, single support contract.<\/li>\n
  • Any two units can be linked<\/strong> over ConnectX-7 for ~405B-class work when needed.<\/li>\n
  • Four desks, four wall sockets \u2014 no rack, no server room, no facility spend<\/strong>.<\/li>\n
  • Replaces contended cloud GPU credits with fixed, predictable INR capital cost<\/strong>.<\/li>\n<\/ul>\n

    AI workload fit<\/h3>\n
      \n
    • Per-developer inference<\/strong> and model experimentation.<\/li>\n
    • Individual fine-tuning<\/strong> without queueing for shared resources.<\/li>\n
    • RAG<\/strong> development against private data on each seat.<\/li>\n
    • Agentic AI<\/strong> and application development at team scale.<\/li>\n
    • Onboarding and training<\/strong> new AI engineers on real hardware.<\/li>\n
    • Pairing two seats for larger evaluation runs when required.<\/li>\n<\/ul>\n

      AI workload positioning<\/h3>\n

      \u26a0\ufe0f Read this honestly:<\/strong> this is four independent machines<\/em>, 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<\/strong> \u2014 four engineers each working uninterrupted on their own hardware \u2014 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.<\/p>\n

      Industry use cases<\/h3>\n
        \n
      • Enterprise & GCCs:<\/strong> equip an applied-AI or platform team with per-seat compute.<\/li>\n
      • Research & education:<\/strong> a teaching lab or research group with dedicated per-user systems.<\/li>\n
      • BFSI:<\/strong> a quant or data-science pod working on confidential data on-premises.<\/li>\n
      • Healthcare:<\/strong> clinical-informatics teams working within residency boundaries.<\/li>\n
      • Media & entertainment:<\/strong> per-artist generative workstations for image and video.<\/li>\n<\/ul>\n

        Performance & how to be sure<\/h3>\n

        The metric that matters here is engineer-hours unblocked<\/strong>, 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”<\/strong> 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.<\/p>\n

        Series & upgrade path<\/h3>\n

        CARINA<\/strong> entry tier. Related: the DGX Spark 2-Node Linked Cluster<\/strong> (~405B on a single problem). Above: QUASAR\/DRACO aiDAPTIV+ workstations<\/strong> for heavier per-seat fine-tuning, and RDP GPU servers when the team needs one large shared model rather than four independent ones.<\/p>\n

        On-prem vs cloud (TCO)<\/h3>\n

        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.<\/p>\n

        Software & day-one readiness<\/h3>\n

        Each unit ships with NVIDIA DGX OS<\/strong> and the full NVIDIA AI stack \u2014 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.<\/p>\n

        Power, thermal & acoustics<\/h3>\n

        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.<\/em><\/p>\n

        Deployment, warranty & support<\/h3>\n

        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 \u2014 typically 4\u20136 weeks, subject to allocation.<\/p>\n

        Why RDP<\/h3>\n

        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.<\/p>\n

        Buy with confidence<\/h3>\n

        Use Request a Quote<\/strong> to reach an RDP solution architect for sizing, a benchmark-your-model session, financing options and a delivery plan. No obligation.<\/p>\n","protected":false},"excerpt":{"rendered":"

        4\u00d7 DGX Spark \u00b7 512GB unified total \u00b7 per-developer AI seats \u00b7 desktop<\/p>\n","protected":false},"featured_media":12478,"comment_status":"open","ping_status":"closed","template":"","meta":{"_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"","rank_math_title":"NVIDIA DGX Spark 4-Seat Team Pod \u2014 Per-Developer On-Prem AI | RDP GPU Mart","rank_math_description":"Four NVIDIA DGX Spark units as independent per-developer AI seats: 512GB unified memory in total, ~200B class each, fully on-premises. Request a quote from RDP GPU Mart.","_hermes_jsonld":""},"product_brand":[],"product_cat":[22],"product_tag":[],"class_list":["post-12701","product","type-product","status-publish","has-post-thumbnail","product_cat-agentic-ai-pcs-edge-ai","pa_form-factor-desktop","pa_gpu-model-nvidia-gb10-grace-blackwell","pa_industry-bfsi-hft","pa_industry-enterprise-gccs","pa_industry-healthcare-life-sciences","pa_industry-media-gaming-entertainment","pa_industry-research-higher-education","pa_series-carina","pa_use-case-agentic-ai","pa_use-case-computer-vision","pa_use-case-fine-tuning","pa_use-case-generative-ai","pa_use-case-inference","pa_use-case-nlp-speech","pa_use-case-rag","pa_workload-fit-per-developer-on-prem-ai-seats","first","instock","taxable","shipping-taxable","product-type-external"],"_links":{"self":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product\/12701","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/comments?post=12701"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media\/12478"}],"wp:attachment":[{"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/media?parent=12701"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_brand?post=12701"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_cat?post=12701"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/rdp.in\/gpu-mart\/wp-json\/wp\/v2\/product_tag?post=12701"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}