QUASAR 192-Core Arm Agentic Inference Server

Overview

The QUASAR 192-Core Arm Agentic Inference Server is a power-efficient Arm server built for high-concurrency agentic inference — a 192-core AmpereOne CPU that runs large fleets of AI agents, orchestration, RAG retrieval and quantised small-model inference at scale, without a GPU for every workload. It brings agent fleets in-house on energy-efficient cores, on-premises, in INR, on a GST invoice — and is GPU-ready when you need acceleration.

Engineered for platform teams running many concurrent agents and RAG pipelines, it pairs 192 Arm cores with 768 GB of memory and fast NVMe, delivering high throughput-per-watt for orchestration-heavy and concurrency-bound agentic workloads — with PCIe slots to add GPUs for accelerated inference.

Key highlights

• 192-core AmpereOne Arm CPU — massive concurrency for agent fleets, orchestration and RAG retrieval, with high performance-per-watt.
• 768 GB DDR5 ECC — large memory for many concurrent agents, vector search and caches.
• GPU-ready (PCIe) — add NVIDIA L40S / RTX PRO accelerators when a workload needs GPU inference.
• 8 TB NVMe NVMe + 2× 25 GbE — fast local storage and high-throughput networking; no egress.
• 2U rack, redundant PSU, BMC/IPMI — production node with lights-out remote management.
• Energy-efficient — high throughput-per-watt for sustained agentic inference, lowering operating cost.
• On-prem data sovereignty — prompts and data 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.

AI workload fit (what it actually runs — honestly)

• High-concurrency agents (primary): run large fleets of AI agents, tool-use loops and orchestration on the 192 Arm cores — workloads that are concurrency- and I/O-bound rather than GPU-bound.
• RAG & retrieval: vector search, embedding lookup and retrieval pipelines at scale.
• Quantised small-model inference: serve quantised small LLMs on CPU for many concurrent sessions; add GPUs for larger models.
• Engineering note: this is a CPU-forward Arm server — it runs the Arm software stack and excels at concurrency-bound agentic and retrieval workloads, not GPU-bound large-model inference. It is GPU-ready: add accelerators for GPU inference. We help you place the right workloads on CPU vs GPU.

AI workload positioning

This sits at the agent-fleet serving stage: the efficient node that runs orchestration, retrieval and high-concurrency agents. With 192 Arm cores and 768 GB of memory, it is sized to sustain large agent fleets at high throughput-per-watt — complementing GPU servers that handle the model-heavy inference.

Industry use cases

• SaaS / product — host large agent fleets and orchestration efficiently.
• BFSI — concurrency-heavy document and retrieval agents under data-residency rules.
• Neocloud / AI providers — efficient agent-serving capacity per watt.
• Government & PSU — sovereign agentic infrastructure on GeM.
• Research & analytics — large-scale retrieval and orchestration.
• Telecom — high-concurrency edge/core agent services.

Performance — and how to be sure

We don’t publish inflated peak numbers. The honest picture: 192 Arm cores and 768 GB are sized for high-concurrency agent fleets, orchestration and retrieval, with optional GPUs for accelerated inference. Want certainty? Request a free benchmark of your agents and request mix on this exact configuration before you buy; we’ll send back real concurrency, throughput-per-watt and latency.

Series & upgrade path

• QUASAR (performance local-AI tier) — this.
• Pairing: use this for agent-fleet serving alongside GPU Servers for model-heavy inference.
• When to add GPUs: populate the PCIe slots with L40S / RTX PRO accelerators for GPU inference — talk to an architect about the CPU/GPU split.

On-prem vs cloud — the TCO case

For sustained agent fleets, an efficient Arm server lowers both per-token API cost and per-agent power cost: you own the capacity, with no egress and full data residency. RDP pricing is fixed in INR with a GST input-credit-eligible invoice — ask for a cost-per-agent and throughput-per-watt comparison.

Software & day-one readiness

Ships ready to serve agents: the Arm Linux stack, Docker, with vLLM / llama.cpp (Arm-optimised), a vector DB and popular agent frameworks pre-configured on Ubuntu LTS for Arm. Optional managed agent-serving stack and observability.

Power, cooling & rack integration

A 2U air-cooled node with redundant PSUs and high performance-per-watt — specify rack power and cooling. (Exact PSU rating, BTU and airflow figures confirmed on the build sheet.) BMC/IPMI provides remote power, console and health monitoring.

Deployment, warranty & support

• Made to order, built and burned-in in India; lead time confirmed at quote.
• In the box: server, rails, power cables, quick-start, and the pre-installed agent-serving 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 an efficient Arm agentic inference server, made to order — talk to an RDP solution architect, size the CPU/GPU split for your agents, and benchmark your own workload on it before you commit. Request a quote to begin.

Specifications