GPU provisioning, distributed training, cost management, and experiment orchestration. We build the training infrastructure that lets your ML team focus on models instead of fighting with compute.
Discuss your training infrastructure needsA training job that should take 2 hours takes 18 because nobody right-sized the GPU instances. A job running overnight fails at hour 15 with no checkpoint saved. A team of five data scientists are all competing for the same 4 GPUs. Spot instances terminate without warning and lose three days of compute. These aren't edge cases — they're the daily reality of teams without purpose-built training infrastructure.
Multi-user GPU pools with job scheduling and fair-share allocation. Your data scientists submit jobs, the scheduler assigns available GPUs, and nobody sits idle waiting for compute. Supports on-demand, spot, and reserved instances with automatic failover when spot instances terminate.
Multi-GPU and multi-node training for models too large for a single GPU. Data parallelism, model parallelism, and pipeline parallelism strategies depending on model architecture. We handle the distributed training framework (PyTorch Distributed, Megatron, DeepSpeed) so your team focuses on model development.
Hyperparameter search across thousands of configurations with automated tracking. Early stopping based on validation metrics. Experiment versioning tied to model versions. Results stored and queryable so you can reproduce any training run and compare across experiments.
Automatic checkpointing at configurable intervals. Resumable training from any checkpoint. Smart checkpoint selection — keep the best model, not just the last one. Integration with model registry so the best checkpoint automatically becomes the candidate for production.
We don't do web apps on the side. Every engineer on your project has deep AI specialisation and has deployed production ML systems before.
We don't implement the first architecture that works. We explore options, test assumptions, and design the solution that fits your specific constraints.
Our AI-augmented methodology compresses delivery timelines by 2-3x compared to traditional consulting.
Timeline
4-8 weeks for initial setup, ongoing optimisation
Team
1-2 senior ML infrastructure engineers
Deliverables
GPU cluster with job scheduler, distributed training framework, experiment tracking infrastructure, checkpoint management, cost monitoring dashboard, runbook documentation
After launch
Optional retainer for ongoing cluster optimisation and scaling as your training needs grow
A computer vision team was training models on a single p3.2xlarge instance because nobody had time to set up distributed training. A single experiment took 72 hours. The team was spending more time waiting for training to complete than iterating on model improvements. We designed a multi-node GPU cluster with job scheduling, implemented distributed data parallel training, added automated hyperparameter search across 200 configurations per week instead of 5, and implemented spot instance management with checkpoint-based failover. Training time dropped from 72 hours to 4 hours per experiment. The team ran 40x more experiments in the same calendar time and improved model accuracy by 12%.
Representative of a typical engagement.
Automatic checkpointing every 15 minutes (configurable). When a spot instance terminates, the job scheduler detects the failure, relaunches the job on a new instance, and resumes from the last checkpoint. The team loses at most 15 minutes of compute, not hours or days.
We can start with a single multi-GPU instance and build up. The infrastructure scales with your needs — starting simple and adding complexity (multi-node, spot management, distributed training) as your team and model sizes grow.
Yes. We work with MLflow, Weights & Biases, Neptune, TensorBoard, or custom tracking. If you have existing experiment data, we migrate it into the new infrastructure so nothing is lost.