Built for Your Largest Enterprise AI Workloads
Scale your AI training, machine learning pipelines and industrial HPC simulations via fully customizable on-demand superclusters engineered for consistent, uninterrupted high throughput.
Why BTI Clusters
BTI dedicated clusters deliver elastic, high-throughput computing for all your generative AI, ML and numerical simulation projects.
Our fully isolated rack environments guarantee stable sustained performance and flexible resource allocation, ideal for long-running training jobs and large-scale batch workloads.
Scalable Performance
Seamlessly expand your compute fleet from dozens up to hundreds of high-performance GPUs and CPUs on demand.
Trusted & Secure
Enterprise-grade zero-trust security architecture, binding uptime SLAs, and 24/7 dedicated technical support for production workloads.
Cost-Efficient Clusters
Transparent pay-per-use billing, you only pay for consumed compute resources — zero hidden platform or bandwidth fees.
Enterprise Security Features
Private encrypted VPN tunnel access, immutable audit logging, and full-stack compliance tooling for end-to-end operational data security.
Clusters Are Best For
Ongoing Large-Scale AI Workloads
You regularly train massive, complex models including NLP, computer vision and recommendation systems. Multiple internal teams and data scientists rely on steady, reliable GPU and CPU compute resources for continuous iteration.
High-Performance Computing Use Cases
Scientific research, industrial simulation and big-data analytics requiring multi-node distributed architectures. Get locked-in stable performance and fully customized cluster hardware layouts tailored to your research pipelines.
Enterprises with Sustained Demands
You need guaranteed stable compute capacity, formal enterprise SLAs and reliable technical support for mission-critical AI production workloads. Fixed & flexible pricing tiers scale alongside your resource consumption to fit your long-term infrastructure budget.
Benefits of BTI Clusters
Flexible Hardware
Custom-tune your exact CPU/GPU ratio, paired with bespoke networking and persistent storage layouts. Scale compute capacity up or down to match each project stage, eliminating idle wasted hardware costs.
Transparent Billing
Live real-time cost monitoring to align spending with actual resource usage and avoid unexpected overspending. Comprehensive utilization dashboards deliver full visibility for performance and financial analysis.
Full Integration & Toolchain Support
Native compatibility with mainstream ML frameworks (TensorFlow, PyTorch) and HPC libraries. Fully optimized for Docker, Kubernetes and all containerized workflows to streamline one-click deployment.
Expert Guidance & SLA-Backed Support
24/7 technical assistance from local HPC & AI engineering specialists to tune cluster throughput and optimize training efficiency. Enterprise-grade uptime SLAs deliver reliable guarantees for core business-critical AI projects.
Explore Our On-Demand GPU Instances If
You Only Run Small or One-Off GPU Jobs
Occasional model fine-tuning and lightweight inference workloads that fit within a single GPU instance; early-stage prototype and small experimental projects with irregular, low-volume compute demands.
You're Experimental or Short-Term
For teams that only require short, ad-hoc bursts of computing power. Standard on-demand GPU instances cut unnecessary fixed infrastructure costs for temporary research tasks.
You Don't Need Guaranteed Capacity
If your workflow can tolerate spot instance scheduling and variable resource availability, a full dedicated cluster brings redundant, over-provisioned capacity you will not fully utilize.