Stop assembling infrastructure. Start running AI workloads.
Acquiring GPU is only the beginning. Before your first workload runs, you're configuring GPU Drivers, CUDA, Kubernetes, schedulers, and model deployment pipelines. Telox eliminates that assembly overhead. From GPU infrastructure to Kubernetes-native runtime, Kueue-based GPU scheduling, Metis LLMOps/MLOps, and Signium security governance — delivered as a single cloud experience.
A GPU alone doesn't solve your AI operations problem.
As generative AI, LLM, and multimodal workloads proliferate, GPU demand is rising fast. But securing GPU capacity is just step one. After that comes the configuration burden: GPU Drivers, CUDA, Kubernetes orchestration, GPU scheduling, and model deployment pipelines — each requiring direct setup. And once you're running, training, fine-tuning, and inference deployment remain fragmented across separate tools. Telox delivers the full AI-Native GPU Cloud stack as a single service — from GPU infrastructure to AI workload runtime. A pre-integrated environment and unified operations toolchain so your teams can focus on running AI workloads, not assembling the infrastructure beneath them.
From GPU infrastructure to AI operations platform — one integrated cloud stack.
The conventional path involves procurement, OS installation, CUDA configuration, Kubernetes setup, GPU scheduler tuning, and model deployment tooling — each a sequential bottleneck. Telox pre-integrates this entire stack, connecting workload submission through to inference serving in a single operational flow.
Full-Stack Integration
GPU infrastructure through AI operations platform — unified as a single cloud experience. A pre-configured runtime environment means AI workloads can be submitted immediately, with no infrastructure assembly required.
GPU-Aware Scheduling
Kueue-based GPU scheduling applies Queue, Quota, Priority, and Preemption policies to manage multi-team GPU resources consistently. Select Whole-GPU or MIG partitioning based on workload characteristics — training, fine-tuning, or inference.
AI Lifecycle Unity
Metis LLMOps/MLOps connects dataset management, training, fine-tuning, evaluation, and inference deployment in a single operational flow. Trained models flow directly into serving endpoints — no handoff gap.
Enterprise Governance
Signium Unified Control Plane provides vertically integrated operations and security controls across the entire Telox stack. IAM, Log, Audit, Alert, KMS, and Security Group policies apply consistently to every workload.
Telox handles the infrastructure. You focus on the AI workload.
Contact UsA full-stack AI-Native GPU Cloud built on five pillars
GPU INFRASTRUCTURE — NVIDIA H200 · B200 · B300
High-performance NVIDIA GPU infrastructure, available on demand. The H200 (Hopper), B200 (Blackwell), and B300 (Blackwell Ultra) lineup covers large-scale AI training, fine-tuning, and inference workloads. GPU availability and configuration details are provided via consultation.
SCHEDULING — Kueue-Based GPU Scheduling
Queue, Quota, Priority, and Preemption policies manage multi-team GPU resources with consistent governance. Fair-share allocation and preemption reduce waste and enforce workload priorities across teams. Whole-GPU or MIG partitioning selected to match workload type.
AI OPERATIONS — Metis LLMOps/MLOps
Dataset management, model training, SFT/DPO fine-tuning, evaluation, and inference deployment — connected in a single operational flow. Trained and tuned models extend directly to Serving Endpoints, eliminating the gap between model development and production services.
KUBERNETES — Pre-Configured Runtime Environment
OS, GPU Driver, CUDA, and Container Runtime come pre-configured in a Kubernetes-native runtime environment. Submit workloads and monitor execution status from the console or API — no infrastructure setup required.
GOVERNANCE — Signium Unified Control Plane
IAM (RBAC · SSO · MFA), Log, Audit, Alert, Notification, KMS, Firewall, and Security Group are vertically integrated across the entire Telox stack. Track which team ran which workload and apply consistent security policies across every environment.
Built for teams that need the complete AI workload environment.
Integrated AI Training · Fine-Tuning · Inference
Dataset management through training, SFT/DPO fine-tuning, model evaluation, and inference service operation — all in a single environment.
Multi-Team GPU Resource Management
Manage training, fine-tuning, and inference workloads from multiple teams with Queue, Quota, and Priority policies.
LLM · Foundation Model Development
High-performance GPU runtime and Metis-based AI operations tooling for large language model training and fine-tuning.
Inference Service Production Operations
Deploy trained and tuned models as serverless or reserved inference Endpoints.
Finance · Public Sector · Regulated AI Workloads
Signium Unified Control Plane applies access control, audit logging, and security policies consistently across the stack.
Enterprise-Wide AI Operations Without Building an Internal Platform
Access a full enterprise AI operations environment through Telox — without building an internal AI platform.
No infrastructure assembly bottleneck. Start from workload submission.
Telox eliminates the assembly overhead of GPU procurement, Kubernetes runtime configuration, GPU scheduler tuning, and AI operations tool integration — pre-integrated into a single cloud stack.
NVIDIA's latest GPU lineup, ready on demand
GPU infrastructure through AI Operations — pre-integrated
Training · fine-tuning · inference in one operational flow
Submit workloads without infrastructure assembly
From GPU infrastructure to AI Operations — one cloud experience.
Talk to a Thaki Cloud solutions architect about the Telox configuration optimized for your training, fine-tuning, and inference workloads.