USE CASES

Large Language Model Training

Training LLMs at 7B, 13B, 70B, or 405B parameter scales requires coordinating hundreds of GPUs with tensor parallelism, pipeline parallelism, and gradient checkpointing. Deepiix handles the distributed coordination layer, ensuring near-linear scaling efficiency as you add nodes. Our users achieve 85–92% MFU on A100 and H100 clusters for standard transformer architectures.

Computer Vision at Scale

Vision transformers, diffusion models, and large-scale contrastive learning (CLIP-style) workloads require high-throughput data pipelines alongside GPU compute. Deepiix integrates with your data loading stack and co-schedules data preprocessing alongside compute jobs to eliminate GPU idle time caused by data starvation — a common source of 20–30% efficiency losses in vision training.

Multi-Modal AI Systems

Multi-modal training — combining text, image, audio, and video — creates scheduling complexity because different modalities have vastly different computational profiles. Deepiix's heterogeneous scheduler handles mixed-workload clusters, routing compute-intensive components to the most appropriate hardware and balancing the cross-modal attention operations that dominate VRAM consumption.

RLHF and Fine-Tuning

Reinforcement learning from human feedback (RLHF) involves multiple models running simultaneously — a reference model, an actor, and a reward model — with complex synchronization requirements. Deepiix orchestrates multi-model training jobs, handles the actor rollout phase efficiently, and makes full-parameter and LoRA fine-tuning jobs first-class citizens in the scheduling queue.

DEPLOYMENT ENVIRONMENTS

CLOUD GPU

AWS, GCP, and Azure GPU instances. Deepiix reduces cloud GPU spend through spot-instance-aware scheduling and preemption-safe checkpointing — making interruptible instances viable for long training runs.

ON-PREMISE CLUSTERS

On-prem GPU clusters — DGX A100, SuperPOD, or custom builds. Deepiix's topology-aware scheduler understands your NVSwitch fabric, InfiniBand topology, and storage hierarchy to maximize job throughput.

HYBRID CLOUD

Span training jobs across on-premise and cloud resources seamlessly. Deepiix's unified scheduler treats cloud and on-prem GPUs as a single resource pool, routing jobs based on cost, latency, and availability.

Solutions