QUASAR 2× RTX PRO 6000 Blackwell Agentic AI PC

Overview

The QUASAR 2× RTX PRO 6000 Blackwell Agentic AI PC runs many AI agents locally — a tower workstation with 2× NVIDIA RTX PRO 6000 Blackwell (192 GB GDDR7) built to run private LLM agents, RAG and automation at scale on your own hardware, offline if needed. It puts serious agentic AI on the desk without sending prompts or data to the cloud — in INR, on a GST invoice.

Engineered for teams running multiple concurrent agents or larger local models, it pairs a high-core Xeon W with two Blackwell GPUs, plus large memory and fast NVMe, so local models and agent fleets respond instantly and keep data in-house.

Key highlights

• 2× RTX PRO 6000 Blackwell · 192 GB GDDR7 — run larger local LLMs (70B+) and many concurrent agents on-device.
• Dual Blackwell GPUs + Xeon W — data-parallel serving of multiple models or larger quantised models for agent fleets.
• Intel Xeon W-3500 + 512 GB DDR5 ECC — high-core orchestration for many multi-step agents and RAG.
• 16 TB NVMe NVMe — large local model store, vector DB and document corpus; no egress.
• Tower, 25 GbE — quiet desk-side footprint with high-throughput networking.
• Private & offline-capable — prompts, data and models stay on-device; air-gappable.
• Make-in-India OEM — predictable INR pricing, GST tax invoice (HSN 8471), pan-India onsite support, GeM-procurable.
• Upgrade path — step up to an agentic inference server for fleet-scale concurrent agents, or an AI Workstation for heavy fine-tuning.

AI workload fit (what it actually runs — honestly)

• Local agents at scale (primary): run 70B+ models and many concurrent agentic workflows — tool use, RAG, automation and copilots.
• RAG & document AI: on-device retrieval over large local corpora and vector DBs.
• Fine-tuning, vision & speech: QLoRA fine-tuning, local CV, speech and multimodal inference.
• Engineering note: with 192 GB GDDR7 of GPU memory this serves 70B+ quantised models; the two PCIe GPUs (no NVLink) are ideal for data-parallel multi-model serving — for one model larger than 96 GB use tensor parallelism across the pair. It is built for responsive local agent fleets, not large-scale training.

AI workload positioning

This sits at the local-agent-fleet stage: the machine that runs many AI agents privately, on the desk. With 192 GB GDDR7 across 2 Blackwell GPUs and a high-core Xeon W, it is sized to sustain concurrent local inference and agent loops — where cloud APIs are slow, costly or non-compliant.

Industry use cases

• Software & product teams — private coding copilots and multi-agent development.
• BFSI & legal — confidential document AI and agent fleets under data-residency rules.
• Healthcare — on-device clinical assistants keeping PHI local.
• Design & media — local creative and generative AI at scale.
• Government & defence — air-gapped private agent fleets.
• Research — a shared local-AI workstation for a team.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 192 GB GDDR7 across 2 GPUs is sized to run 70B+ models and many concurrent agents locally. Want certainty? Request a free benchmark of your agents and models on this exact configuration before you buy; we’ll send back real tokens/sec, concurrency and latency.

Series & upgrade path

• QUASAR (performance local-AI tier) — this.
• Ladder: mini/edge → agentic AI PC → multi-GPU agentic PC → agentic inference server.
• When to step up: for fleet-scale concurrency, move to an agentic inference server; for heavy fine-tuning, an AI Workstation — talk to an architect.

On-prem vs cloud — the TCO case

Running an agent fleet locally removes per-token API costs and keeps data on-device: for steady agentic use, a one-time machine beats a recurring cloud bill, with full privacy. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a cost-per-agent comparison vs cloud APIs.

Software & day-one readiness

Ships ready to run agents: NVIDIA driver, CUDA, cuDNN, Docker, with Ollama / vLLM / Triton, a local vector DB and popular agent frameworks pre-configured on Ubuntu or Windows. Optional managed local-AI stack and model library.

Power, cooling & rack integration

A tower workstation with quiet desk-side operation — specify a dedicated circuit for the dual-GPU configuration. (Exact power draw, thermal and acoustic figures confirmed on the build sheet.)

Deployment, warranty & support

• Made to order, built and burned-in in India; lead time confirmed at quote.
• In the box: system, power supply, quick-start, and the pre-installed local-AI software stack.
• Onsite warranty + AMC with pan-India coverage and an RMA/escalation path (exact terms 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 local-AI machine for running agent fleets privately, made to order — talk to an RDP solution architect, size it for your agents and models, and benchmark your own workload on it before you commit. Request a quote to begin.

Specifications