Production-Ready ML Infrastructure
GPU clusters, high-speed storage, pre-configured frameworks, and expert support. We build and manage the compute layer so your team focuses on models.
Infrastructure Shouldn\'t Be Your ML Team\'s Job
Your ML engineers should be training models, not debugging CUDA installations, configuring distributed training, or figuring out why data loading is slow.
Cloud GPU providers give you bare hardware. You still need to build everything on top—networking, storage, frameworks, monitoring. That\'s weeks of work before you train anything.
What We Do Instead
We build complete ML infrastructure: GPU clusters configured for distributed training, storage architected for throughput, frameworks installed and tested, monitoring in place.
Your team gets a ready-to-use platform. SSH in, start training. We handle the infrastructure so you ship models.
Complete ML Infrastructure
Everything you need to train models at scale.
GPU Cluster Setup
Multi-GPU configurations for distributed training. Single-node multi-GPU or multi-node clusters based on your needs.
Storage Architecture
High-throughput NVMe for training data, object storage for datasets. No data pipeline bottlenecks.
Network Infrastructure
Low-latency interconnects between GPUs. NVLink within nodes, high-speed networking between nodes.
ML Frameworks
Pre-installed PyTorch, TensorFlow, JAX environments. CUDA toolkit configured and tested.
Job Orchestration
Queue management, resource allocation, job scheduling. Run experiments without stepping on each other.
GPU Monitoring
Utilization, memory, thermal monitoring. Know when GPUs are idle or overloaded.
Security
Isolated environments per team or project. Encrypted data at rest and in transit.
ML-Native Support
Engineers who understand training runs, not just servers. We speak PyTorch.
What You Get
Pre-configured and tested.
Pick the Right GPU for Your Workload
We help you choose—and we don\'t upsell.
NVIDIA H200
NVIDIA H100
NVIDIA A100
NVIDIA L40S
Who This Is For
Teams Scaling Beyond Single-GPU
Your experiments work on one GPU, but training takes too long. We build multi-GPU setups that actually accelerate training, with proper distributed training configuration.
Research Labs
Need multi-node training for large experiments? We configure clusters with proper interconnects and shared storage that multiple researchers can use without conflict.
Computer Vision Teams
Training on large image/video datasets requires serious storage throughput. We architect systems where data loading never starves your GPUs.
NLP & Foundation Model Work
Fine-tuning or pre-training language models? We set up the memory, storage, and distributed training infrastructure for large sequence lengths and big batches.
Common Questions
What GPUs do you offer?
NVIDIA H200, H100, A100 (40GB and 80GB), and L40S. We help you pick the right GPU for your workload—we don't upsell H100s when A100s will do the job.
Can you set up multi-node distributed training?
Yes. We configure multi-node clusters with proper networking, shared storage, and distributed training frameworks (PyTorch DDP, DeepSpeed, etc.). We test the setup before handoff.
What ML frameworks come pre-installed?
We set up PyTorch, TensorFlow, JAX, and the Hugging Face ecosystem. CUDA, cuDNN, and TensorRT are configured and tested. We can add other frameworks you need.
How does job scheduling work?
We can set up Slurm, Kubernetes, or simpler queue systems depending on your team size and workflow. The goal is letting multiple people run experiments without conflicts.
Do you provide experiment tracking tools?
We configure your environment to work with MLflow, Weights & Biases, or other tracking tools you use. Storage is set up for artifact logging.
What about data storage for large datasets?
We architect storage with high-throughput NVMe for active training data and S3-compatible object storage for datasets. No data loading bottlenecks.
Next Steps
Let\'s Design Your ML Infrastructure
Tell us about your workload—models, team size, data volumes. We\'ll design infrastructure that actually fits.