AI Infrastructure & Capabilities
Accelerate Generative AI, Large Language Models (LLMs), and computer vision: NVIDIA HGX H100 / A100 Tensor Core GPU supercomputing clusters linked with 3.2Tbps Quantum-2 InfiniBand, automated MLOps pipelines, and TensorRT real-time edge inference.
Our AI Supercomputing Engineering Lifecycle
High-density liquid-cooled rack staging, InfiniBand fabric, and model tuning
AI Workload & Thermal Sizing
Auditing target LLM parameter size, FP8 / FP16 precision requirements, liquid cooling CDU capacity, and power density (40kW+ per rack).
HGX H100 & InfiniBand Staging
Racking 8-GPU HGX H100 nodes, configuring NVLink 900GB/s GPU-to-GPU bridges, and calibrating Quantum-2 NDR 400Gbps InfiniBand switches.
MLOps & TensorRT Optimization
Deploying Kubernetes GPU operator, tuning Slurm / Kubeflow ML pipelines, and quantizing INT8 models for real-time edge inference.
NVIDIA HGX H100 / A100 & INFINIBAND
GPU Compute Infrastructure
Ultra-scale NVIDIA HGX H100, H200, and A100 80GB SXM5 GPU supercomputing nodes. Interconnected with 900GB/s NVLink interconnects and Quantum-2 NDR 400Gbps InfiniBand switches for distributed LLM pre-training and fine-tuning.
| GPU Architecture | NVIDIA HGX H100 / H200 (80GB/141GB HBM3e) SXM5 8-GPU Baseboards |
|---|---|
| GPU Interconnect | NVIDIA NVSwitch 4th Gen with 900GB/s Bi-Directional Bandwidth |
| High-Speed Fabric | Quantum-2 NDR 400Gbps InfiniBand & Spectrum-4 800G Ethernet Switches |
| Power & Cooling | Direct-to-Chip (D2C) Liquid Cooling CDU Ready (Up to 80kW/Rack) |
MLOps Platform
End-to-end Machine Learning Operations (MLOps) workflow automation. Streamline training data ingestion, experiment tracking, automated hyperparameter tuning, model registry versioning, and enterprise data governance.
| Pipeline Orchestration | Kubeflow Pipelines, MLflow, Slurm Workload Manager & Ray.io |
|---|---|
| Model Registry | Centralized Version Control with Automated Lineage & Compliance Audit |
| Data Governance | Role-Based Data Access Control (RBAC), Feature Store & Anonymization |
| Monitoring & Drift | Real-Time Data Drift, Concept Drift & Model Performance Telemetry |
MLOPS PIPELINE & GOVERNANCE
TENSORRT REAL-TIME INFERENCE
Accelerated AI Inference
Ultra-low latency real-time model serving at scale. Optimized with NVIDIA TensorRT, Triton Inference Server, and ONNX Runtime for high-throughput video analytics, speech recognition, and LLM text generation.
| Inference Engine | NVIDIA TensorRT-LLM, Triton Inference Server & ONNX Runtime |
|---|---|
| Model Quantization | FP16, INT8 & INT4 Calibration with Zero Accuracy Loss |
| Edge GPU Hardware | NVIDIA Jetson AGX Orin & L4 / L40S PCIe Accelerator Cards |
| Latency Benchmark | Sub-5 Millisecond Computer Vision & 100+ Tokens/Sec LLM Streams |
Request AI Cluster Proposal
Speak with a certified Microlines AI Supercomputing Architect for GPU cluster sizing.
Enterprise AI GPU & MLOps Infrastructure Bundles
NVIDIA HGX H100 8-GPU SXM5 Nodes, Quantum-2 InfiniBand Switches & TensorRT Edge Hardware.
NVIDIA HGX H100 8-GPU Supercomputing Node
8x H100 80GB SXM5 GPUs, 900GB/s NVLink NVSwitch, 4x Quantum-2 NDR 400G InfiniBand, D2C liquid cooling ready.
NVIDIA Quantum-2 NDR 400G InfiniBand Switch
64 NDR 400Gb/s ports, sub-500ns port latency, In-Network Computing (SHARPv3) for fast distributed LLM gradient aggregation.
NVIDIA Jetson AGX Orin 64GB Industrial AI Box
NVIDIA Ampere architecture, 2048 CUDA cores, 64 Tensor cores, 64GB LPDDR5, sub-5ms TensorRT computer vision inference.
Kubeflow & Slurm Enterprise MLOps Stack
Pre-configured Slurm workload manager, Ray.io distributed training, automated model registry versioning, feature store governance.
Frequently Asked Questions
Direct-to-Chip liquid cooling requirements, NVLink 900GB/s bandwidth, and TensorRT model quantization.