DRACO 8× RTX PRO 6000 aiDAPTIV+ AI Server

Overview

768 GB of VRAM, eight GPUs, and still no liquid cooling. The densest air-cooled node RDP builds: eight NVIDIA RTX PRO 6000 Blackwell cards with a 16 TB aiDAPTIV+ NVMe cache, delivering 180B+ capability into a standard rack without a CDU, a facility-water loop, or a datacenter rebuild.

Key highlights

• 8× NVIDIA RTX PRO 6000 Blackwell 96GB — 768 GB aggregate VRAM.
• 16 TB aiDAPTIV+ cache (8× 2TB U.2) — several very large models resident simultaneously.
• 180B+ inference and fine-tuning at multi-tenant concurrency.
• 1 TB DDR5 ECC on a dual-Xeon host — the data pipeline keeps up with the GPUs.
• Air-cooled 4U — the key practical advantage over SXM nodes that demand liquid.
• 2× 100GbE plus BMC/Redfish out-of-band management.
• Deployable in an existing server room, on-premises, DPDP-aligned.

AI workload fit

• High-concurrency inference for 180B+ models.
• Departmental and enterprise fine-tuning as a shared service.
• Large-scale production RAG with many simultaneous users.
• Agentic AI back-ends running continuously.
• Generative AI, computer vision, NLP & speech at service scale.
• Sovereign AI deployments where liquid cooling is not feasible on site.

AI workload positioning

The pragmatic ceiling for organisations whose facilities cannot take liquid cooling. You trade the interconnect bandwidth of an SXM/NVLink node for eight PCIe cards that run on ordinary rack airflow — an excellent trade for inference and parameter-efficient fine-tuning, a poor one for large multi-GPU pretraining. We will tell you honestly which side of that line your workload sits on. aiDAPTIV+ extends usable model memory onto high-endurance enterprise NVMe, so a model class that would normally demand far more GPUs runs on the GPUs you actually own. It is a capacity technology, not a bandwidth one — streaming offload is tuned for inference and parameter-efficient fine-tuning, and the final numbers are confirmed in a scoped proof-of-concept, not promised on a datasheet.

Industry use cases

• Public sector & sovereign: large-model capability in government facilities without water.
• BFSI: high-concurrency document and risk inference inside the bank’s own DC.
• Healthcare: hospital-group AI services with strict residency requirements.
• Defence & aerospace: air-gapped deployments in constrained facilities.
• Enterprise & GCCs: a shared AI platform node for multiple business units.

Performance & how to be sure

Eight PCIe cards plus a 16 TB cache gives exceptional resident-model capacity; sustained multi-GPU training throughput will be below an equivalent NVLink/SXM node, and we would rather say so up front than have it surface after purchase. 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

Top of the air-cooled server line. Below: QUASAR 4× RTX PRO 6000. Above, where the facility supports it: H200 NVL and H200/B200/B300 SXM nodes with NVLink/NVSwitch for true multi-GPU training, then rack-scale GB300 and AMD systems.

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

Ships workload-ready: Ubuntu LTS or RHEL, NVIDIA driver + CUDA + cuDNN, container runtime, Kubernetes/Slurm integration, and the aiDAPTIV+ memory-management layer — PyTorch-compliant, no model-code changes. Standard serving stacks (vLLM, SGLang, TensorRT-LLM) supported, plus the aiDAPTIVPro toolchain for ingest, RAG, fine-tune, monitor, validate and inference.

Power, thermal & acoustics

4U with eight high-TDP GPUs and redundant PSUs — high per-rack power density on standard PDU feeds; a thermal survey of the intended rack is recommended before installation. 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 8 weeks.

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