AI Inference &
Model Serving
RDP is building India’s sovereign AI Inference & Model Serving infrastructure — optimised GPU servers, low-latency networking, and production-grade serving stacks for deploying AI models at scale. From GenAI chatbots to real-time vision inference.
Why AI Inference & Model Serving, Why Now
As India’s AI ecosystem matures, the bottleneck is shifting from training to inference. Every AI application — GenAI chatbots, recommendation engines, vision systems.
Target Segments
Enterprise AI Teams
Production deployment of GenAI, recommendation, NLP, and vision models for.
AI Startups & SaaS
Inference backend for AI-powered products, APIs, and services serving Indian and global.
Government & PSUs
Sovereign inference for citizen AI services, document processing, and national AI.
Telecom & Media
Content recommendation, real-time moderation, speech AI, and personalisation at telecom.
Healthcare & BFSI
Regulated inference for medical AI, fraud detection, and financial risk models with data.
AI Startups & Industry R&D
Private sector R&D labs, AI product companies, deep-tech startups
Full Stack Architecture
Three integrated layers — hardware, software, and AI — purpose-built for research at institutional, state, and national scale.
INTELLIGENCE — Optimised AI Models
LLM Serving · Vision Inference · Speech AI · Recommendation · NLP · Multimodal
SOFTWARE — Model Serving Platform
Triton Server · vLLM · Load Balancer · Model Registry · Monitoring · API Gateway
HARDWARE — RDP Proprietary Infrastructure
AI-POD · Inference GPU Server · Model Cache · Lossless Fabric · Edge Nodes · HA Cluster
RDP Proprietary Infrastructure
| Component | RDP SKU | Inference Role | Key Specification |
|---|---|---|---|
| Inference Cluster | RDP AI-POD (Rack Scale) | Multi-model inference serving at scale with auto-scaling | 8× GPU per node, NVLink |
| Inference Server | RDP Inference AI SKU | Optimised for low-latency, high-throughput model serving | L40S / A100 / H100 options |
| Model Cache | RDP NVMe All-Flash Array | Fast model loading, KV-cache, and inference dataset storage | Up to 200 TB, 20 GB/s |
| Network Fabric | RDP Lossless Fabric | Ultra-low latency interconnect for distributed inference | 100GbE / 400GbE |
| Edge Inference | RDP Inference Edge | On-site inference for latency-critical applications | Compact GPU, 24×7 |
Model Serving Platform
NVIDIA Triton Server
Multi-framework model serving with dynamic batching and model ensemble
vLLM / TGI
Optimised LLM inference engines with PagedAttention and continuous batching
NVIDIA TensorRT
GPU inference optimisation — quantisation, layer fusion, and kernel auto-tuning
KServe / Seldon
Kubernetes-native model serving with canary deployment and A/B testing
Prometheus + Grafana
Inference monitoring — latency, throughput, GPU utilisation, and SLA tracking
NGINX / Envoy
API gateway, rate limiting, and load balancing for inference endpoints
LLM Serving (GenAI)
Production deployment of Llama, Mistral, Gemma, and custom LLMs with streaming
Vision Inference
Real-time object detection, classification, and segmentation for production vision AI
Speech & Language AI
ASR, TTS, and NLP inference for conversational AI and document processing
Recommendation Engine
Real-time recommendation serving for e-commerce, media, and personalisation
Multi-Model Orchestration
Chained inference pipelines — RAG, agent workflows, and ensemble models
Model Optimisation Service
Quantisation, pruning, distillation, and TensorRT conversion for inference efficiency
Pre-Validated AI Models
| Inference Domain | Model Type | Application | Performance |
|---|---|---|---|
| LLM / GenAI | vLLM + TensorRT-LLM | Llama 3, Mistral, Gemma serving with continuous batching and PagedAttention | 100+ tokens/sec, <100ms TTFT |
| Vision AI | TensorRT + Triton | Object detection, segmentation at production scale with dynamic batching | <10ms per image, 1000 img/sec |
| Speech AI | Whisper + XTTS | Speech-to-text and text-to-speech for Indian languages | Real-time, 12+ languages |
| Recommendation | NVIDIA Merlin | Deep learning recommendation models for real-time personalisation | <5ms latency, 50K QPS |
| NLP / Embedding | Sentence Transformers | Text embedding, classification, and NER for document processing | 10K embeddings/sec |
| Multimodal | LLaVA / CLIP Serving | Vision-language model serving for multimodal AI applications | <200ms per query |
Deployment Configurations
Three pre-validated tiers — each with hardware, software, AI models, and RDP SLA support. Custom BOQ on request.
Starter
Single Application / Startup
Professional
Multi-Application Enterprise
Enterprise
Platform / National Scale
End-to-End on Sovereign Infrastructure
Complete pipeline from data ingestion to actionable intelligence — every step on RDP infrastructure.
REQUEST
BALANCE
INFERENCE
PROCESS
DELIVER
& LOG
Build With Us · Sell With Us
RDP’s Research AI platform is designed for India’s ecosystem. We’re inviting technology and channel partners, and direct inquiries from organisations.
Technology Partners
- Certify your serving stack on RDP inference hardware
- Access GPU labs for optimisation benchmarking
- Joint go-to-market with RDP AI team
- Co-branded solution briefs for enterprise procurement
- API gateway and monitoring integration support
Channel Partners
- Sell complete AI inference solutions
- Pre-configured inference deployment packages
- RDP-backed implementation & SLA support
- Partner margins on hardware + software
- AI deployment training & certification
Organisations Deploying AI
- Schedule an inference architecture workshop
- Request a benchmark on your models
- Get a custom Bill of Quantities
- Evaluate starter tier with your workload
- GeM / enterprise procurement support
India’s Sovereign Research AI Infrastructure
Make in India Hardware
All RDP systems designed and assembled in India. GeM-listed for institutional procurement.
Research Data Sovereign
Research data, model weights, and IP stay on Indian institutional infrastructure. Zero export.
NVIDIA Certified Stack
DGX-Ready validated, CUDA optimised, and certified for HPC and AI research workloads.
DST / MeitY Aligned
National science and technology mission aligned. Eligible for research infrastructure funding.
5-Year Lifecycle Commitment
Hardware support, HPC engineering, and continuous performance optimisation throughout lifecycle.
Full Stack — Single OEM
Servers, storage, networking, software, and AI from one Indian OEM. One BOQ, one SLA.
Regulatory Alignment
| Standard | Scope | RDP Coverage |
|---|---|---|
| DPDP Act 2023 | Data Protection | On-premise inference — zero cross-border transfer of user data or model outputs |
| IT Act | Information Technology | Compliant deployment for Indian information technology regulations |
| ISO 27001 | Information Security | RDP infrastructure ISO 27001 certified |
| SOC 2 Ready | Security Controls | Infrastructure supports SOC 2 Type II audit requirements |
| GFR / GeM | Government Procurement | GeM-listed for government and PSU procurement |
| NVIDIA Certified | GPU Validation | NVIDIA-validated inference configurations for production workloads |
Projected Impact
| Metric | Before RDP AI | After RDP AI | Impact |
|---|---|---|---|
| Inference cost | Cloud: ₹5–15/1K tokens | On-prem: ₹0.5–1/1K tokens | 10× cheaper at scale |
| Latency | Cloud: 200–500ms | On-prem: <10–50ms | 5–10× faster |
| Data privacy | API vendor exposure | 100% on-premise | Zero exposure |
| Availability | Cloud SLA 99.9% | On-prem 99.99% | Higher uptime |
| Cost predictability | Variable, per-token | Fixed monthly | No bill shock |
| Vendor lock-in | Cloud API dependent | Open-source stack | Full portability |
Ready to Build Research AI Capability?
From pilot to production — RDP designs, builds, and deploys sovereign AI infrastructure for India’s research ecosystem.
Trademark Notice: All product names, logos, and brands mentioned are property of their respective owners. NVIDIA, CUDA, L40S, A100, H100, H200 are trademarks of NVIDIA Corporation. Use is for identification only.
Disclaimer: RDP Technologies provides AI compute infrastructure. Research outcomes, model performance, and scientific conclusions are the responsibility of the deploying research organisation.
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