GPU compute on demand. No queue.
Provision GPU clusters in minutes, track costs per experiment, and auto-scale based on demand. Stop waiting for GPUs and start training — with full visibility into what every model run actually costs.
AI infrastructure pain points
GPU compute is the bottleneck for every AI team. Scarcity, cost opacity, and manual setup slow down innovation and burn through budgets.
GPU Scarcity
H100s and A100s are expensive and perpetually oversubscribed. Teams wait days or weeks for GPU access while training windows close. Procurement takes months, and cloud GPU pricing is unpredictable.
No Cost Visibility Per Experiment
GPU compute burns through budgets fast, but no one knows which experiments, teams, or models are consuming what. Cost attribution is manual, inaccurate, and always retroactive.
Manual Cluster Setup
Every new training job requires manual environment setup — CUDA drivers, framework versions, dataset mounting, network configuration. What should take minutes takes hours of DevOps time.
Long Wait Times
Without proper scheduling and preemption, GPU resources sit idle between jobs while other teams wait in informal queues. Utilization rarely exceeds 40% despite constant complaints about capacity.
How PLATFORMA helps
Self-service GPU infrastructure with per-experiment cost tracking, auto-scaling, and native MLOps integration.
On-Demand GPU Provisioning
Request GPU resources through a self-service portal or API. Select GPU type (A100, H100, L40S), quantity, and framework — get a ready-to-train environment in under 2 minutes. No tickets, no waiting.
Per-Experiment Billing
Every training run, fine-tuning job, and inference deployment is tracked with per-second GPU billing. Cost attribution down to the individual experiment, team, and project. Real-time spend dashboards.
Auto-Scaling GPU Pools
GPU pools scale automatically based on queue depth and priority. When demand spikes, additional GPUs are allocated from reserve capacity. When jobs complete, resources are reclaimed instantly.
MLOps Integration
Native integration with MLflow, Weights & Biases, and Kubeflow. Experiment tracking, model versioning, and pipeline orchestration work out of the box with your GPU infrastructure.
Persistent Storage
High-performance NVMe storage for datasets and model checkpoints. Shared storage volumes that persist across training jobs. Snapshot and backup capabilities for critical model artifacts.
Container-Native Workloads
Run training jobs as containers with GPU passthrough. Pre-built images for PyTorch, TensorFlow, JAX, and HuggingFace. Custom images supported. Full Kubernetes compatibility.
Real-world scenarios
How AI teams use PLATFORMA to manage GPU compute at scale — from startups training LLMs to enterprises running inference at the edge.
AI Startup Scales Training
An AI startup training large language models provisions 64 A100 GPUs for a multi-day training run. Per-experiment cost tracking shows exactly how much each model iteration costs. When training completes, GPUs are released instantly — no wasted spend.
Research Lab Shares GPU Cluster
A university AI research lab with 30 researchers shares a pool of 16 GPUs. Fair-share scheduling ensures every researcher gets access based on priority and quota. Real-time queue visibility eliminates conflicts.
Enterprise Runs Inference at Edge
An enterprise deploys inference models on edge GPU nodes for real-time computer vision. PLATFORMA manages the full lifecycle — model deployment, scaling, monitoring, and rollback — across distributed GPU infrastructure.
GPU compute benchmarks
What AI teams achieve with PLATFORMA GPU infrastructure.
GPU provisioning
From request to ready
Experiment costs
Per-second GPU billing
Scaling
Dynamic GPU pool management
GPU support
A100, H100, L40S, and more
Ready to scale GPU compute?
Stop waiting for GPUs. Provision on demand, track every experiment, and auto-scale your infrastructure as your models grow.
Common Questions
The platform supports any GPU that works with NVIDIA CUDA and the NVIDIA Container Toolkit. This includes A100, H100, L40S, A10G, T4, and RTX series GPUs. Multi-GPU configurations (up to 8 GPUs per node) and multi-node training with NVLink and InfiniBand interconnects are supported. We also support AMD Instinct GPUs through ROCm.
The platform uses the NVIDIA GPU Operator and device plugin for Kubernetes. GPU resources are exposed as schedulable resources in Kubernetes. You can request specific GPU types, quantities, and memory in your pod specs. Multi-GPU and fractional GPU (MIG) scheduling is supported. Native integration with Kubeflow for ML pipeline orchestration.
Yes. The platform supports tiered GPU pricing — on-demand (guaranteed, higher price), reserved (committed use, discounted), and preemptible (can be reclaimed, lowest price). Preemptible jobs automatically checkpoint and resume when capacity is available. You set your budget and priority — the platform optimizes scheduling.
The platform provides persistent NVMe storage volumes that survive across training jobs. Datasets are mounted as shared volumes accessible from any GPU node. Model checkpoints are saved to persistent storage automatically. You can also connect external storage (S3-compatible, NFS, Ceph) for large datasets. Snapshot and backup capabilities protect critical artifacts.
Engineering culture
Short reads that sharpen your engineering instincts and help you stay ahead of the curve.