Reduce GPU compute costs by 60% while scaling your training workloads from a single GPU to 10,000-node clusters — intelligently.
Intelligent workload scheduling eliminates idle GPU time and bin-packs training jobs to maximize hardware utilization across your entire cluster.
Scale seamlessly from a single GPU to 10,000-node clusters without configuration changes. Deepiix abstracts infrastructure complexity so your team focuses on models.
Automatic checkpoint-and-restore recovers failed training runs from the last saved state — no wasted compute, no lost progress on long-running experiments.
Monitor all training jobs across clouds and on-premise hardware from a single pane of glass. Track GPU utilization, job status, and resource costs in real time.
Hand-tuned CUDA kernels squeeze maximum throughput from every GPU generation — A100, H100, and custom silicon — without requiring manual kernel engineering.
Built-in experiment tracking automatically logs hyperparameters, metrics, and artifacts for every run — making it trivial to reproduce results and compare experiments.
Deepiix was founded to solve the most pressing bottleneck in AI development: the enormous cost and complexity of training large-scale deep learning models. Our platform gives every ML team — from three-person startups to global enterprise AI labs — access to infrastructure that was previously available only to the largest cloud providers.
We believe world-class GPU infrastructure should be accessible, efficient, and invisible. Engineers should be building better models, not managing Kubernetes clusters.
Our platform combines intelligent workload scheduling, CUDA-level optimization, and a distributed checkpoint system built for the realities of large model training. We integrate directly with PyTorch, JAX, and TensorFlow without requiring code changes.
Deepiix's scheduling engine continuously monitors cluster utilization and rebalances jobs in real time — eliminating the idle GPU time that accounts for up to 40% of wasted compute budgets at most AI organizations.
We take a hardware-first approach to ML infrastructure. Our team includes former GPU architects from NVIDIA and ML platform engineers who have run training at ByteDance, OpenAI, and Google DeepMind.
That experience shapes every design decision — from the scheduler's topology-aware placement algorithms to the checkpoint compression strategy that reduces storage costs by 70%.
Former VP Infrastructure at OpenAI with a PhD in Distributed Systems from ETH Zurich. Anna leads Deepiix's product vision and engineering strategy.
Led GPU infrastructure at NVIDIA for 8 years and is a recognized CUDA optimization expert. Ryan architects Deepiix's kernel-level performance layer.
Architected ByteDance's ML training platform, which processed over 10,000 daily training jobs. Mei leads Deepiix's scheduler and distributed systems team.
Join leading AI teams using Deepiix to run faster, cheaper, and at unlimited scale.
Get Early AccessHave questions about the Deepiix platform or want to discuss your infrastructure requirements? Our engineering team is ready to help you get started.
140 New Montgomery St, 10th Floor
San Francisco, CA 94105
(415) 555-0174
See how Deepiix can reduce your GPU costs and accelerate model training. Our team will walk you through the platform with your specific workloads in mind.
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