Built by infrastructure engineers who ran ML at scale — and got tired of wasting GPU budgets.
Deepiix was born out of a shared frustration. When Anna Schmidt was running infrastructure at OpenAI, and Ryan Moore was optimizing GPU clusters at NVIDIA, they kept running into the same problem: even the most sophisticated AI organizations were losing 30–50% of their compute budget to scheduling inefficiencies, failed runs, and idle hardware.
The tools that existed were either designed for HPC workloads from a decade ago, or cloud-native abstractions that sacrificed performance for convenience. Neither was built for the reality of training large language models, vision transformers, and multi-modal architectures at scale.
In 2024, Anna, Ryan, and Mei Lin — who had built ByteDance's ML training platform serving over 10,000 daily training jobs — founded Deepiix to build the infrastructure layer they always wished had existed.
Today, Deepiix's platform powers deep learning training for companies ranging from early-stage AI startups to enterprise AI labs, delivering 60% reductions in compute costs without requiring engineers to change a line of model code.
We optimize at the hardware layer — kernel-level CUDA tuning, topology-aware scheduling, and memory hierarchy exploitation — not just at the orchestration layer.
Infrastructure should not be a black box. Every scheduling decision, every kernel dispatch, and every resource allocation is visible in our unified dashboard.
We build for the engineers who run training jobs — the people who understand what a failed run at hour 47 of a 48-hour job really costs a team.
We are currently onboarding AI teams with serious GPU workloads. Apply for early access today.
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