AI Infrastructure

Overview

Remangu AI Infrastructure provides managed H100 GPU clusters purpose-built for training and deploying machine learning models at scale. The platform handles cluster provisioning, distributed training orchestration, data pipeline management, and inference endpoint deployment, enabling AI teams to focus on model development rather than infrastructure operations.

Whether training large language models, fine-tuning diffusion models for content generation, or running inference workloads for real-time AI features in games and media applications, the infrastructure adapts to workload demands. Auto-scaling provisions GPU capacity when training jobs launch and releases it upon completion, ensuring teams access the compute they need without paying for idle resources.

Key Features

• H100 GPU Clusters — NVIDIA H100 Tensor Core GPUs connected via NVLink and NVSwitch deliver up to 3,958 TFLOPS of FP8 performance per node. Clusters scale from single-node experimentation to multi-node distributed training with hundreds of GPUs.
• Training Optimization — Distributed training frameworks—DeepSpeed, FSDP, Megatron-LM—are pre-configured and tuned for H100 hardware. Mixed-precision training, gradient checkpointing, and communication optimization reduce time-to-convergence and maximize GPU utilization.
• Data Pipeline Automation — Managed data pipelines ingest, transform, validate, and version training datasets. Pipelines support streaming from object storage, real-time feature computation, and integration with data labeling platforms. Dataset lineage is tracked automatically.
• MLOps Integration — Native integration with MLflow, Weights & Biases, and custom experiment tracking systems. Model registry, A/B testing infrastructure, and automated rollback support production deployment workflows from training through serving.
• Cost-Efficient Scaling — Spot instance strategies, cluster scheduling, and resource pooling across teams minimize GPU costs. Idle detection automatically pauses non-active training jobs, and reserved capacity planning locks in rates for predictable baseline workloads.

Technical Specifications

Specification	Detail
GPU	NVIDIA H100 80GB (SXM5)
Interconnect	NVLink 4.0 + NVSwitch, 900 GB/s per GPU
Scaling	Auto-scaling, single-node to multi-hundred GPU
Storage	High-throughput parallel filesystem (Lustre)
Frameworks	PyTorch, JAX, TensorFlow, DeepSpeed, Megatron-LM
MLOps	MLflow, W&B, custom registry integration
Networking	Elastic Fabric Adapter (EFA), 3200 Gbps

How It Works

1. Provision — Define cluster requirements through the Remangu console or IaC templates: GPU count, storage capacity, networking configuration, and framework versions. Clusters provision in minutes with all dependencies pre-installed.
2. Ingest — Connect data sources to managed pipelines that handle extraction, transformation, validation, and versioning. Datasets are stored on high-throughput parallel filesystems optimized for the sequential read patterns common in training workloads.
3. Train — Launch distributed training jobs with a single command. The orchestrator handles node allocation, process placement, fault detection, and automatic checkpoint recovery. Experiment metrics stream to your tracking platform in real time.
4. Deploy — Promote trained models to managed inference endpoints with configurable auto-scaling, A/B traffic splitting, and latency-based routing. Canary deployments and automated rollback protect production workloads from regression.