Intelligent GPU infrastructure for serious deep learning workloads.
Deepiix is a full-stack deep learning infrastructure platform. It sits between your model code and your GPU hardware, handling scheduling, checkpointing, monitoring, and kernel-level optimization — so your team can focus entirely on model development.
Deepiix's scheduler continuously analyzes your GPU cluster topology and training job profiles to make optimal placement decisions. Jobs are bin-packed based on memory footprint, communication patterns, and expected runtime — eliminating the fragmentation that leads to idle GPUs. The scheduler handles preemption gracefully, using our checkpoint system to save and restore state when higher-priority jobs arrive.
Our kernel library provides drop-in replacements for common deep learning operations — attention, layer normalization, embedding lookups, and custom fused operations — that are hand-tuned for each GPU generation. On A100s, our fused attention kernel achieves 94% of theoretical peak FLOPS. On H100s with FP8 support, we achieve near-theoretical throughput for transformer training workloads without requiring any changes to user model code.
Failed training runs are the silent killer of ML productivity. Deepiix checkpoints model state incrementally — saving only the delta since the last checkpoint — and compresses it with a custom algorithm that achieves 70% size reduction on typical transformer checkpoints. Recovery is automatic: when a node fails or a preemption occurs, the training job resumes from the last checkpoint with zero user intervention required.
Per-GPU utilization, memory bandwidth, thermal throttling, and NVLink saturation — all visible in real time with 1-second granularity across every node in your cluster.
Every training run automatically logs hyperparameters, loss curves, gradient norms, and custom metrics. Compare experiments side-by-side and reproduce any past run with one click.
Granular cost attribution per team, project, and experiment. Identify which training runs are burning budget and which scheduling policies deliver the best cost-per-FLOP.
Our team will walk you through the platform using your actual workloads and cluster specs.
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