AI and IT infrastructure, engineered to the workload
Infrastructure that performs the way your engineers need it to.
TriniTix AI designs, builds and operates custom, high-performance infrastructure for HPC, EDA and AI, on premises, in the cloud and across both. Every layer is sized and tuned to your technology, not to a template.
One engineered stack
Designed as one system, operated by one accountable partner.
What TriniTix changes for your infrastructure
Generic infrastructure makes engineers wait. Ours is measured against the jobs it carries, then tuned until it does not.
Performance by design
Compute, storage, network and acceleration are each specified for the workload they serve. Nothing is oversubscribed by default, and nothing is idle by accident.
Throughput per watt, per rupee
Operated, not just installed
We run what we build. Remote managed services keep the estate monitored, patched and capacity-planned, and every release is an opportunity to tune it further.
One team, end to end
Compliant from day one
Access, data and AI usage are governed by policy rather than by goodwill, with audit trails your board and your customers can rely on.
Visibility the board can read
Six domains. One engineered stack.
HPC, EDA and AI ask different things of the same hardware. Cloud, network and hybrid multi-cloud decide where that hardware lives and how it is reached. We design for each, then operate them together.
HPC
Simulation and compute at scale
Clusters, schedulers and accelerators arranged for sustained throughput, so long-running simulations finish on the plan, not after it.
- Cluster and scheduler design
- Parallel storage and low-latency fabrics
- Capacity planning and cost control
Tools we work with: Slurm, PBS Pro, IBM LSF, OpenMPI, Intel MPI, InfiniBand, RoCE, Lustre, BeeGFS, IBM Storage Scale, Spack, Warewulf
EDA
Chip design environments that keep tapeouts on schedule
License-aware compute, low-latency storage, session management and remote visualization for design teams working across sites.
- EDA tool and license infrastructure
- Identity, access and secure remote design
- Data centers close to the design team
Tools we work with: Synopsys, Cadence and Siemens EDA flows, FlexNet (FlexLM), Altair Accelerator and Monitor, IBM LSF, NetApp, VAST, NICE DCV, NoMachine, FreeIPA, RHEL, Rocky Linux
AI
Training and inference platforms you own
GPU and accelerator fabrics, orchestration and data pipelines, with governance over how models and data are used across the company.
- Accelerator selection and fabric design
- Model serving and orchestration
- AI usage policy and data protection
Tools we work with: NVIDIA H100/B200, AMD Instinct, CUDA, ROCm, NCCL, Kubernetes, Slurm, Ray, Kubeflow, PyTorch, vLLM, Triton Inference Server, MLflow, WEKA, MinIO
Cloud
Governed cloud foundations, with HPC and GPUs on demand
Landing zones, infrastructure as code and cost discipline, so cloud capacity is a lever you pull deliberately rather than a bill you discover.
- Landing zones, IAM and network design
- Cloud HPC and GPU clusters for burst capacity
- FinOps: tagging, forecasting and right-sizing
Tools we work with: AWS, Microsoft Azure, Google Cloud, Oracle Cloud, Terraform, OpenTofu, Ansible, EKS, AKS, GKE, AWS ParallelCluster, Azure CycleCloud, Google Cluster Toolkit
Network
Data center fabrics, campus networks and a secure edge
Spine-leaf fabrics for east-west traffic, a separate low-latency fabric for HPC and AI, and campus Wi-Fi that engineers do not notice.
- Spine-leaf data center design (VXLAN/EVPN)
- InfiniBand and RoCE fabrics for HPC and AI
- Wi-Fi 6E/7 campus, firewalls and zero-trust access
Tools we work with: Cisco Nexus and Catalyst, Arista, Juniper, NVIDIA Quantum and Spectrum, Cisco Meraki, Aruba, Palo Alto Networks, Fortinet, Infoblox, NetBox
Hybrid multi-cloud
One estate across sites, data centers and clouds
SD-WAN and private interconnects join the pieces; one identity, one observability stack and one deployment pipeline make them behave as a single system.
- SD-WAN, Direct Connect, ExpressRoute and Interconnect
- Federated identity, GitOps and unified monitoring
- Data placement, backup and disaster recovery
Tools we work with: Cisco Catalyst SD-WAN, Fortinet Secure SD-WAN, VMware VeloCloud, Azure Arc, Google Anthos, Argo CD, Flux, Okta, Microsoft Entra ID, Prometheus, Grafana, Zabbix, Veeam
Nine capabilities, delivered as one system
Each layer is a discipline in its own right. The value is in how they are chosen and tuned together.
Virtualization
Right-sized virtual and bare-metal estates, so critical jobs never share more than they should.
Typical stack: VMware vSphere, KVM, Proxmox VE, Nutanix, OpenStack, KubeVirt, Podman
SD-WAN
Secure, software-defined connectivity between offices, data centers and clouds, managed as one network.
Typical stack: Cisco Catalyst SD-WAN, Meraki, Fortinet, VeloCloud, Versa, IPsec overlays, SASE
Storage
Parallel and tiered storage matched to the IO patterns of design data, simulation output and training sets.
Typical stack: NetApp ONTAP, Dell PowerScale, Pure Storage, VAST, WEKA, Lustre, Ceph, MinIO, NVMe-oF, NFS, S3
Compute
CPU fleets specified for the instruction mix and memory footprint your tools actually use.
Typical stack: Intel Xeon, AMD EPYC, Arm, RHEL, Rocky Linux, Ubuntu, Warewulf, xCAT, Ansible, Lmod, NUMA tuning
Acceleration
GPUs and dataflow accelerators applied where they move the result, without forcing your code to be rewritten.
Typical stack: NVIDIA H100/H200/B200, AMD Instinct, FPGAs, CUDA, ROCm, NVLink, MIG, NCCL, GPUDirect
Orchestration
Schedulers and container platforms that keep utilization high and queues short across every workload.
Typical stack: Slurm, IBM LSF, Altair Accelerator, PBS Pro, Kubernetes, OpenShift, Rancher, Helm, Argo CD, Ray
Middleware
License servers, identity, session brokers and the integration layer that keeps every tool talking.
Typical stack: FlexNet (FlexLM), RLM, Altair Monitor, FreeIPA/IdM, Active Directory, Keycloak, Kerberos, LDAP, NICE DCV, NoMachine, Vault
Network
Low-latency data center fabrics, campus networks and Wi-Fi designed for the traffic engineers generate.
Typical stack: Cisco, Arista, Juniper, spine-leaf, VXLAN/EVPN, BGP, 100/400GbE, InfiniBand NDR, RoCEv2, Wi-Fi 6E/7, 802.1X
Compliance
Policy enforcement, AI data-loss prevention and audit-ready visibility across people, systems and models.
Typical stack: ISO 27001, SOC 2, CIS Benchmarks, DPDP Act, Wazuh, Splunk, CrowdStrike, Tenable, CyberArk, AI-DLP gateways
The reference topology, the tools behind each layer and a plain-language glossary are on the technical architecture page, written for the architects and engineers who will run the estate.
Open the architecture pageHow we engage
Four stages, in order. Each one ends with something you can read, sign off and measure.
Assess
We measure how your workloads behave today: utilization, latency, license contention and cost.
Architect
A design tuned to those measurements, with the trade-offs between performance, cost and risk made explicit.
Build
Delivered in your data center, in partner colocation facilities in India, or in the cloud you already use.
Operate
Remote managed services: monitoring, patching, capacity planning and continuous tuning, with reporting your leadership can act on.
Why teams choose TriniTix
Built by people who have run infrastructure for chip design and AI teams, and who know where the bottlenecks hide.
Hardware that adapts to your software, not the other way round
We select and configure compute and acceleration around the code and tools you already run. Performance should not depend on a porting project.
Governance built in, not bolted on
AI usage policies, data redaction and visibility into how models are used are part of the design, so adoption is fast without being reckless.
Semiconductor-native
EDA tool environments, license management, identity and data center planning for fabless and design-center teams are our home ground.
One accountable partner
From the data center rack to the engineer's desktop, one team designs, builds and operates the estate, and one team answers for it.
Built for the teams building the hardest things
Fabless semiconductor startups
Production-grade EDA infrastructure from the first hire, hosted and managed so the team can focus on silicon.
Chip design centers
Multi-site design environments with low-latency access to compute, storage and licenses across India and abroad.
AI research and product teams
Training and inference platforms you own, with the governance to use them across the company safely.
HPC-dependent engineering
Simulation, modelling and analytics workloads that need predictable throughput at a predictable cost.
Let's size the infrastructure your roadmap needs.
Bring your workload profile, your tapeout or training schedule and your constraints. We will come back with a design and a plan to run it.
Talk to usPhone
+91 88842 11166Office
596, 4th Block Koramangala,
Bengaluru, Karnataka 560034