QUASAR 8× RTX PRO 6000 Blackwell GPU Server

Overview

The QUASAR 8× RTX PRO 6000 Blackwell GPU Server is a Rack 4U rack server built to bring inference into your own data centre. 8 RTX PRO 6000 Blackwell Server Edition GPUs deliver 768 GB GDDR7 of high-bandwidth GPU memory in a dense, serviceable chassis — sized to serve 70B+ at scale-class models and host many models at once, behind your firewall, in INR, on a GST invoice.

Engineered for AI platform and MLOps teams standardising production inference on owned infrastructure, it pairs the GPUs with a 2× Intel Xeon 6 host, 2 TB DDR5 ECC and 30 TB NVMe, with redundant power and full BMC/IPMI remote management — a production node that racks and runs, not a repurposed desktop.

Key highlights

• 768 GB GDDR7 of GPU memory across 8× RTX PRO 6000 Blackwell — serve 70B+ at scale-class models or host many smaller models concurrently.
• Blackwell architecture with FP4 — next-generation inference efficiency and accuracy, ECC throughout.
• 2× Intel Xeon 6 + 2 TB DDR5 ECC — high core count and memory bandwidth to feed 8 data-centre GPUs.
• Rack 4U, redundant PSU, BMC/IPMI — hot-swap drives, tool-less service, lights-out management.
• 30 TB NVMe + 2× 100 GbE — fast dataset, checkpoint and weight storage with high-throughput networking; no egress fees.
• On-prem data sovereignty — data and weights stay in-house; DPDP-friendly, air-gappable.
• Make-in-India OEM — predictable INR pricing, GST tax invoice (HSN 8471), pan-India onsite support, GeM-procurable.
• Scale path — grow within the QUASAR server line and out to RDP rack-scale systems as demand rises.

AI workload fit (what it actually runs — honestly)

• Inference (primary): serve 70B+ at scale-class models, or host multiple 7B–34B models concurrently for high aggregate throughput.
• Fine-tuning: QLoRA / LoRA up to ~70B+ and full fine-tuning of 7B-class models, data-parallel across the GPUs.
• RAG, vision, multimodal & agentic: production RAG endpoints, vision/multimodal inference and multi-agent back-ends on the 30 TB NVMe.
• Engineering note: the RTX PRO 6000 Blackwell Server Edition is a PCIe card with no NVLink — the 8 GPUs are ideal for data-parallel serving and multi-instance hosting; for a single model larger than 96 GB, use tensor parallelism across cards, or step up to an SXM/NVLink node. A production serving workhorse, not a large-scale training fabric.

AI workload positioning

This sits at the deploy-and-serve stage of the AI lifecycle. With 768 GB GDDR7 of GPU memory, a dense PCIe layout, a 2× Intel Xeon 6 host and fast NVMe, it is sized to sustain production inference traffic with predictable latency — where renting equivalent cloud GPUs around the clock becomes the dominant line in an AI budget.

Industry use cases

• BFSI — private model endpoints for fraud, risk and document intelligence under data-residency rules.
• Healthcare & life sciences — on-prem clinical NLP and imaging inference, PHI in-house.
• Government & PSU — sovereign AI on GeM-procurable infrastructure.
• SaaS / product — own your serving stack instead of per-token APIs.
• Telecom & manufacturing — AI close to operations.
• Research & higher-ed — a shared institutional inference node.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 768 GB GDDR7 of GPU memory across 8 GPUs is sized to serve 70B+ at scale-class models and many concurrent smaller models on-prem. Want certainty? Request a free benchmark of your models and request mix on this exact configuration before you buy — we’ll send back real tokens/sec, concurrency and latency for your workload.

Series & upgrade path

• QUASAR (performance inference tier) — this.
• GPU-count ladder: 2-GPU → 4-GPU → 8-GPU within the line; step up to NVLink/SXM nodes for tensor-parallel training.
• When to step up: for multi-rack scale, move to RDP Rack-Scale AI Systems and AI SuperClusters — talk to an architect about the fabric.

On-prem vs cloud — the TCO case

For sustained inference, owning beats renting: 8 continuously-running cloud GPUs of this class add up fast, and on-prem removes egress fees and keeps data and weights in-house. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a 3-year TCO comparison against your current cloud spend.

Software & day-one readiness

Ships pre-configured to serve: NVIDIA driver, CUDA, cuDNN, NCCL, Docker and NVIDIA Container Toolkit, with PyTorch, vLLM / Triton / TensorRT-LLM on Ubuntu LTS. Optional Slurm/Kubernetes, managed AI-stack and observability setup available.

Power, cooling & rack integration

A Rack 4U air-cooled node with redundant PSUs and substantial power draw — specify rack power and cooling capacity; liquid-cooling available on request. (Exact PSU rating, BTU, airflow and rack-depth figures confirmed on the build sheet.) BMC/IPMI provides remote power, console and health monitoring.

Deployment, warranty & support

• Made to order, built, racked-and-stacked and burned-in in India; realistic lead time confirmed at quote.
• In the box: server, rails, power cables, quick-start, and the pre-installed AI software stack.
• Onsite warranty + AMC with pan-India coverage and an RMA/escalation path (exact term & response window confirmed at quote).

Why RDP

14 years of Make-in-India infrastructure and 300,000+ devices shipped. Indian OEM, INR pricing, GST tax invoice (HSN 8471), pan-India onsite engineers, GeM availability, and DPDP / sovereign-AI-ready deployment.

Buy with confidence

This is a production inference server, made to order — talk to an RDP solution architect, get a configuration and 3-year TCO tailored to your workload, and benchmark your own models on it before you commit. Request a quote to begin.

Specifications