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Home / RDP AI Labs
RDP AI Labs · v1.0
Build AI capability—not just AI infrastructure.
We design, deploy, validate and operate secure AI environments for training, inference, RAG, fine-tuning, agents and sovereign workloads—on infrastructure we manufacture, integrate and support in India.
Engineering-led. Vendor-pragmatic. Quoted in INR with an itemised bill of materials.
The problem worth solving
Boards approve GPU budgets faster than engineering organisations can absorb them. The gap between "we bought the cluster" and "the cluster serves a workload that a business unit depends on" is where most AI capital quietly stalls. RDP AI Labs exists to close that gap.
01 · CAPITAL
Configurations get frozen before anyone has profiled the actual workload—context length, batch shape, concurrency, checkpoint cadence, data gravity. The result is a cluster that is memory-bound where it should be compute-bound, or a fabric that throttles the moment training goes multi-node.
02 · UTILISATION
A rack that draws power is not a rack that earns. Between silicon and served tokens sit drivers, container images, collective communication tuning, schedulers, storage throughput, thermal headroom and failure handling. Each one silently taxes utilisation, and together they decide your real cost per million tokens.
03 · ACCOUNTABILITY
The OEM blames the integrator, the integrator blames the fabric vendor, the software partner blames the drivers, and the CIO owns the outcome regardless. Splitting an AI programme across five suppliers moves risk to the only party who cannot escalate it.
We treat an AI lab as one engineered system—facility, silicon, fabric, storage, runtime and workload—with a single party accountable from bill of materials to service level.
Definition
An AI lab is a controlled environment where an organisation develops, benchmarks, fine-tunes, serves and governs AI systems—with reproducible results and known cost. It is six layers, and a weakness in any one of them shows up as a bad number in all of the others. We design, build and operate all six.
Service catalogue
Every service below produces something a technical evaluator can inspect—an architecture, a bill of materials, a benchmark report, a runbook—not a slide deck. Engagements can be taken individually or sequenced into a programme.
We profile the workload first, then size the machine. Context length, batch shape, concurrency, checkpoint cadence and data gravity drive the configuration—not the other way round.
Deliverable: architecture pack + BOM
Validated single systems for local development, fine-tuning and inference—built and burned in at our own facility, so the machine that arrives is the machine that was specified.
Deliverable: deployed, benchmarked system
Multi-GPU and multi-node environments where the fabric is designed for the collective pattern the workload actually uses—because that is what decides scaling efficiency, not the GPU count.
Deliverable: accepted, benchmarked cluster
Put your own documents and systems behind a model without the data leaving your boundary. Retrieval quality is treated as an engineering problem with an evaluation set, not a demo.
Deliverable: private RAG service + eval set
For the cases where retrieval and prompting genuinely are not enough. We start by proving that they are not—then adapt the smallest model that clears your evaluation bar.
Deliverable: adapted model + eval report
Turning a model into a service with a latency profile and a unit cost you can put in a budget. Serving strategy, batching and quantisation are chosen against your traffic shape.
Deliverable: production inference service
Agents that touch real business systems need permissions, guardrails and an audit trail before they need autonomy. We build the pilot with the controls already in place.
Deliverable: governed agent pilot
Measure the real workload before committing to scale. We publish the method with the result, so your team can reproduce the number and challenge it.
Deliverable: reproducible benchmark report
Labs decay quietly. Drivers drift, storage fills, utilisation slips and nobody notices until a training run fails at 3 a.m. We keep the environment usable.
Deliverable: operated environment + SLA
Reference architectures
These are reference architectures, not fixed SKUs or quoted prices. They exist so a technical team can locate itself quickly and so a finance team knows roughly which order of magnitude the conversation is in. Final configuration is confirmed after discovery and benchmarking.
Prove the use case before it becomes a capital request.
Internal AI that production teams can actually depend on.
Rack-scale capacity engineered to be sold, shared or scaled.
Engagement model
You can stop after any stage. That is deliberate—it keeps the commercial risk staged and it forces us to earn the next phase with evidence rather than optimism.
Understand the workload, the data, the constraints and who has to sign off.
Design the system across all six layers and price it honestly.
Manufacture, integrate, rack and cable—then burn it in before it ships.
Benchmark against your workload and publish the method with the numbers.
Move it into service with access control, monitoring and a rollback path.
Keep it healthy, keep it utilised, and review capacity on a schedule.
Validation standard
Anyone can quote a datasheet. What decides whether an AI programme works is what the system does on your data, at your concurrency, in your building. These are the metrics we instrument and report during validation—with the harness handed over so your engineers can reproduce and challenge every figure.
| Metric | What it actually tells you | Why it decides the outcome |
|---|---|---|
| Model FLOPs utilisation | How much of the silicon you paid for is doing useful mathematics, as a fraction of peak. | The single most honest efficiency number in training. A low figure means you bought capacity you are not using. |
| Tokens / sec / GPU | Serving throughput normalised to hardware, at a stated batch size and context length. | Normalises vendor claims. It is the only fair way to compare two very different configurations. |
| Time to first token | How long a user waits before anything appears, at target concurrency. | Decides whether an internal copilot gets adopted or quietly abandoned. |
| p99 latency | The tail experience under load, not the average. | Averages hide the failures. Users and SLAs live in the tail. |
| Scaling efficiency | Throughput retained going from one node to N nodes. | Exposes fabric and topology mistakes that no single-node test will ever reveal. |
| Collective bandwidth | Measured all-reduce and all-gather bandwidth across the fabric. | The ceiling on distributed training. Usually the real bottleneck, rarely the one that gets tested. |
| Checkpoint time | How long a write and a restore take at your model size. | Determines how much work a failure costs you, and how often you can afford to save. |
| Job failure rate | Interruptions and restarts per thousand GPU-hours. | Reliability engineering, not marketing. It sets the effective capacity of the cluster. |
| Power per rack | Measured draw under sustained load, with the thermal margin left. | Tells you whether the next rack fits in the building or needs a facility project first. |
| Cost per 1M tokens | Fully-loaded rupee cost of useful output, including power and amortisation. | The number a CFO can compare against an API bill. It is the whole business case in one figure. |
| Retrieval quality | Recall at k and groundedness against a held-out evaluation set, for RAG systems. | Separates a system that answers correctly from one that answers confidently. |
Reference workloads
Each workload has a different bottleneck. Naming it early is what keeps the architecture honest.
Bound by collective bandwidth and checkpoint throughput long before it is bound by GPU count.
Bound by GPU memory and dataset quality. Usually solved with a smaller model than expected.
Bound by KV-cache memory and batching strategy. Latency and cost move in opposite directions.
Bound by retrieval quality and index freshness far more often than by model size.
Bound by tool latency, permissions and error handling. Throughput is rarely the problem.
Bound by data pipeline and decode throughput; the GPU is often waiting on storage.
Bound by interconnect latency and double-precision capability, not by tensor cores.
Embarrassingly parallel and cheap to scale — the ideal first workload on new capacity.
Who we build for
CIO & CISO
Private and on-premises deployment with data resident in India, documented data flows, document-level access control and an audit trail your security review can actually read. A staged pilot means the compliance conversation happens on a real system, not a proposal.
CTO & Head of Engineering
Reference architecture, golden images, the benchmark harness and the runbooks are handed over. No proprietary black box, no dependency you cannot replace. We are vendor-pragmatic and we will tell you where your existing estate constrains the workload instead of proposing a rebuild by default.
Neoclouds & DC operators
The layer between racks and revenue: fabric design, multi-tenant scheduling and isolation, image and driver standardisation, burn-in and acceptance testing, utilisation telemetry, and a hardware supply chain manufactured and supported in India rather than air-freighted and hoped for.
AI labs & research teams
Cluster environments where an experiment run in March reproduces in September—versioned images, tracked runs, a model registry, scheduler quotas that survive contention, and checkpointing designed around your failure budget.
Government & public sector
Sovereign deployment on Make in India hardware, procurable through GeM, with an itemised bill of materials and an acceptance test that is written down before the order is placed. We describe what the system does; we do not claim approvals we do not hold.
Investors & boards
A staged model where each tranche is released against a benchmark, not a milestone slide. Cost per million tokens and utilisation are reported from day one, so the question "is this asset earning?" has a number attached to it every quarter.
Why RDP AI Labs
RDP Technologies Limited has spent fourteen years designing and manufacturing computing hardware in India. AI Labs is the engineering practice built on top of that—which is why the architecture, the machine, the deployment and the support contract can all come from one accountable party.
A 28,000 sq ft facility in Hyderabad, 300,000+ devices shipped and 1M+ end users. When a component needs to change, a thermal design needs revisiting or a board needs replacing at 2 a.m., we are not raising a ticket with someone else's factory.
Rack density, power distribution, liquid cooling and structural loading are part of the design conversation from the first session—because in high-density AI builds the building is usually the constraint that nobody costed.
Make in India hardware, deployment inside a boundary you nominate, data resident in India, and procurement through direct purchase or GeM. Sovereignty here is an architecture and a supply chain, not a marketing adjective.
Every engagement is staged so that capital is committed against a measured benchmark. If the pilot says a smaller configuration is sufficient, that is what we will quote—we would rather size it right than sell it twice.
Itemised bills of materials, rupee pricing, and standard configurations purchasable online through RDP GPU Mart. Procurement and finance can see exactly what they are buying, line by line.
Onsite hardware support, spares, driver and platform lifecycle management, monitoring and quarterly capacity reviews. The lab is still useful in year three, which is when most of the value was supposed to arrive.
Questions we get asked
Plan your AI lab
A short brief is enough to start. An RDP engineer reviews it and comes back with either a set of clarifying questions or a first-pass architecture—not a generic brochure.
RDP AI Labs
It has an owner, a method and a set of numbers. Bring us the workload and we will bring the architecture, the machine, the benchmark and the people who keep it running.
Reliability is Our Product. · Make in India. Built for an AI-Ready India.
