GPU server setup
On-premise or colocated GPU servers configured with drivers, CUDA, containers and scheduling.
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GPU, Model Serving & MLOps
Running AI privately and at scale needs the right infrastructure. We design and set up GPU servers, model serving, vector databases and monitoring on AWS, GCP, Azure or your own data centre, sized for your workloads and budget.
What this covers
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Who it is for
We help you choose between API providers, cloud GPUs and on-premise hardware based on data sensitivity, usage and cost, then build a secure, observable platform your team can operate, with autoscaling so you pay for capacity only when you need it.
Discuss your requirementEnterprises that must keep AI workloads inside India or their own network
AI product companies facing rising API bills
Research and data science teams needing shared GPU capacity
IT teams asked to host open-source LLMs securely
Capabilities
On-premise or colocated GPU servers configured with drivers, CUDA, containers and scheduling.
Managed GPU instances and AI services on AWS, GCP and Azure, including India regions.
High-throughput inference with vLLM, TGI or Ollama behind an authenticated, OpenAI-compatible API.
Vector databases, object storage and pipelines sized for retrieval workloads.
Scale-to-zero, spot capacity, batching and quantisation to cut GPU spend.
GPU utilisation, latency, error and cost dashboards, plus network isolation and access control.
How we work
You always know what happens next, who is responsible and what you will receive at each stage.
Typical timeline
Proof of concept in 3–4 weeks; production rollout typically 6–12 weeks.
Models, users, latency needs and data rules are captured.
Hardware and cloud options are compared on cost and performance.
Infrastructure is built as code with security baselines.
Throughput, latency and cost are measured under load.
Runbooks, monitoring and optional managed support.
Deliverables
Technology & standards
We recommend tools based on your scale, budget and existing systems, not on what is fashionable. Every choice is explained in the proposal.
Engagement models
A 3–4 week pilot on your own data with agreed accuracy targets, so you see real results before scaling.
Hardened integration, guardrails, monitoring and admin controls, delivered in milestones with a fixed quote.
Ongoing prompt and model tuning, evaluation runs, cost monitoring and feature additions on a monthly plan.
How pricing works: AI projects are priced in two stages: a fixed-price proof of concept, then a production quote based on what the pilot proves. Model and API usage costs are estimated upfront and billed at actuals.
Get a quoteFAQs
Cloud GPUs suit variable or early-stage workloads; owned hardware can be cheaper for steady, heavy use or strict data rules. We model both options with your expected usage before you commit.
Yes. Open-source models such as Llama, Mistral or Qwen can run on your own servers with no data leaving your network, served through a private API.
It depends on model size, context length and concurrent users. We benchmark candidate configurations, including quantised models that need less memory, and recommend the most cost-effective option.
Yes. We offer managed operations with monitoring, patching, model updates and cost reviews under a monthly SLA.
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Reply within one business day
Share a few details. A senior specialist reviews them and schedules a call to discuss scope, timeline and cost, with no obligation.
Receive preliminary project architecture, pricing tiers, and timeline estimates within 15 minutes under strict NDA.