Stop wasting GPU cycles on idle time. Our infrastructure is purpose-built to eliminate data loading bottlenecks, maximize utilization, and accelerate model training from hours to minutes.
Machine learning workloads demand infrastructure that can keep pace with rapid innovation. Whether you're training large language models, fine-tuning computer vision systems, or running distributed deep learning experiments, your GPU infrastructure determines whether you iterate in hours or days.
Modern ML teams face a fundamental challenge: acquiring the right GPU resources at the right time without the capital expense and operational overhead of on-premises infrastructure. Cloud GPU hosting provides the computational flexibility needed for research, experimentation, and production deployment—but not all GPU cloud providers are built for the unique demands of machine learning workflows.
The difference between mediocre and world-class ML infrastructure comes down to three factors: raw computational power, data pipeline efficiency, and operational simplicity. GPU Core delivers all three, purpose-built for teams who ship models to production.
Research from Google and Microsoft shows that ML engineers waste up to 70% of expensive GPU time on data loading bottlenecks and infrastructure overhead. Here's what's actually holding back your model training.
The most expensive problem in machine learning infrastructure isn't the GPU cost—it's paying for GPUs that aren't working. Studies show that GPU utilization commonly drops to 15-30% during training runs, with some teams reporting idle times exceeding 66%.
The culprit? Data loading bottlenecks. Your H100s can process billions of operations per second, but if data can't reach them fast enough, they sit waiting. Every second of idle GPU time is wasted money, delayed experiments, and slower time to production.
Modern deep learning models process data faster than traditional storage can deliver it. The problem intensifies with cloud object storage: network latency, the "many small files" problem, and geographical separation between compute and storage create data stalls that cripple GPU performance.
When researchers optimized data loading pipelines, they reduced data loading time from 82% to 1% of total training time—increasing GPU utilization from 17% to 93%. That's the difference between a training run taking 10 hours versus 2 hours.
You provision GPUs expecting one cost, then the bill arrives with surprise charges: peak-time premiums, bandwidth overages, data transfer fees, and storage costs you didn't anticipate. Managing costs becomes a full-time job, pulling engineers away from actual ML work.
Complex pricing models force teams into spreadsheet gymnastics trying to predict actual costs. By the time you realize a workload is over budget, you've already burned through thousands of dollars on inefficient infrastructure.
Modern cloud platforms offer hundreds of services and configuration options. The learning curve is steep: Kubernetes, container orchestration, network configuration, storage optimization, and monitoring. Your ML engineers spend more time as infrastructure engineers than data scientists.
Every hour spent debugging infrastructure, optimizing data pipelines, or wrestling with platform complexity is an hour not spent improving models, experimenting with architectures, or delivering value. Developer friction becomes the bottleneck to innovation.
Scaling training across multiple GPUs introduces new failure modes: communication overhead between nodes, load balancing issues, NCCL timeouts, synchronization bugs, and deadlocks. These problems are notoriously difficult to debug, especially when infrastructure instability compounds application bugs.
Poor network topology and insufficient bandwidth between nodes can limit distributed training efficiency to 60-70% of theoretical maximum—even with perfect application code. Your expensive multi-GPU setup delivers only marginal speedups over single-GPU training.
Data preprocessing happens on CPU before reaching the GPU. When CPUs can't keep up with GPU demand, your training pipeline becomes CPU-bound—GPUs wait idle for the next batch. Complex preprocessing, inefficient data augmentation, and poor CPU-GPU coordination turn expensive accelerators into expensive space heaters.
Optimizing CPU-GPU data transfers, implementing asynchronous operations, and ensuring CPUs never bottleneck GPU processing requires deep infrastructure expertise that most ML teams don't have—and shouldn't need to develop.
We built our infrastructure from the ground up for one purpose: maximize GPU utilization and minimize time to trained model. Here's how we eliminate the bottlenecks that plague ML workloads.
Our infrastructure pairs NVMe storage with GPUDirect Storage capabilities, enabling direct data transfers from storage to GPU memory without CPU involvement. This architectural decision eliminates the primary bottleneck in ML training: waiting for data.
Every node includes high-performance local NVMe and access to high-speed shared storage, ensuring data loading never becomes your limiting factor. Your GPUs stay busy doing what they're designed for: computation.
5.5x improvement in GPU utilization through storage optimization
GPU Node 1 ←──400G──→ Switch ←──400G──→ GPU Node 2
↓ ↓ ↓
NVSwitch Spine NVSwitch
↓ Router ↓
8x H100 HGX 8x H100 HGX
(NVLink mesh) (NVLink mesh)
Latency: <1μs intra-node, <10μs inter-node
Bandwidth: 900GB/s NVLink, 50GB/s node-to-nodeDistributed training performance lives or dies on network bandwidth. Our 400G networking infrastructure ensures GPU-to-GPU communication never becomes the bottleneck, whether you're training across GPUs in a single node or coordinating multi-node distributed workloads.
H100 HGX configurations include NVLink and NVSwitch for 900GB/s intra-node bandwidth, while our high-speed network fabric maintains low latency and high throughput for inter-node communication. This architecture supports efficient data parallelism, model parallelism, and pipeline parallelism.
The best infrastructure is infrastructure you don't think about. We handle the operational complexity—monitoring, maintenance, optimization—so your team focuses on machine learning, not infrastructure engineering.
Deploy containers, run bare metal, or use Kubernetes orchestration. All approaches work seamlessly on our infrastructure with sub-60-second cold starts. Copy-paste Docker commands from documentation, and they run in production exactly as written.
docker run --gpus all \
-v $(pwd):/workspace \
--shm-size=16g \
nvcr.io/nvidia/pytorch:24.01-py3 \
python train.py \
--model llama-3-70b \
--distributed \
--fp16
# That's it. No complex configuration,
# no infrastructure management,
# no surprises.One price. No surprises. Budget with confidence.
Every hour of GPU time costs exactly what we say it costs. No bandwidth charges, no data egress fees, no peak-time premiums, no surprise line items. You see the hourly rate, multiply by hours, and that's your cost.
This transparency matters when planning experiments, budgeting projects, and making architecture decisions. You can calculate costs before running workloads, not after receiving unexpected bills.
Not every workload needs a full GPU. Multi-Instance GPU (MIG) technology on A100 and H100 GPUs allows you to partition a single GPU into multiple isolated instances, each with dedicated compute and memory resources.
Run multiple experiments simultaneously, isolate development from production inference, or maximize utilization across diverse workloads. Our Kubernetes integration makes MIG resource management seamless, without complex manual configuration.
Small workloads, inference, experimentation
Medium models, development environments
Large models, production inference
Full GPU (7g.80gb) available for maximum performance workloads
Not chatbots or tier-1 script readers. Engineers who understand distributed training, tensor parallelism, and gradient accumulation.
Help tuning data pipelines, optimizing batch sizes, and choosing the right distributed training strategy.
Moving from other providers? We help ensure zero downtime and optimize your deployment from day one.
Infrastructure problems rarely announce themselves clearly. A training run that should take 4 hours takes 12. GPU utilization hovers at 40%. NCCL throws cryptic timeouts. These problems require expertise, not documentation links.
Our support team includes engineers who understand the full stack: from hardware topology to framework internals. We help diagnose issues, optimize performance, and ensure you extract maximum value from your GPU investment.
Talk to our engineering teamFrom research experimentation to production deployment, our infrastructure supports the full ML lifecycle.
Train transformer models from scratch or fine-tune foundation models. H100 clusters with NVLink support efficient model and pipeline parallelism for models exceeding 70B parameters.
Train object detection, segmentation, and classification models on large image datasets. High-bandwidth storage ensures image loading never bottlenecks training throughput.
Train RL agents with high sample throughput. Our infrastructure handles the computational demands of policy gradient methods, Q-learning, and environment simulation.
Train SDXL, Stable Diffusion, and custom generative models. L40S GPUs provide optimal performance for image generation workloads at competitive pricing.
Deploy production inference endpoints with vLLM, TensorRT, or Triton. MIG partitioning allows efficient multi-tenant inference serving on shared GPUs.
Run parallel experiments efficiently. MIG slicing allows multiple hyperparameter search trials to run concurrently, accelerating model development cycles.
Hardware and infrastructure specifications that matter for machine learning performance.
From zero to training runs in three simple steps.
Select the GPU type and quantity that matches your workload. H100 for large-scale training, A100 for versatility, L40S for generative AI. Single GPU or multi-node clusters—we support both.
View available configurationsUse our pre-configured Docker images with PyTorch, TensorFlow, JAX, or bring your own environment. Copy-paste commands from your development setup—they run identically in production.
docker run --gpus all nvcr.io/nvidia/pytorch:24.01-py3 python train.py
Launch your training runs with full visibility into GPU utilization, memory usage, and throughput. Integrate with your existing monitoring tools or use standard NVIDIA tooling.
Need help optimizing performance? Our engineering team provides guidance on data pipeline tuning, distributed training strategy, and resource allocation.
Common questions about GPU hosting for ML workloads.
Our infrastructure addresses the primary bottleneck in ML training: data loading. By pairing NVMe storage with GPUDirect Storage capabilities and 400G networking, we minimize GPU idle time waiting for data. This architectural approach consistently achieves 90%+ GPU utilization compared to industry averages of 60-70%. The result is faster training runs and better return on GPU investment.
Stop wasting GPU cycles. Start training faster with infrastructure built for machine learning workloads.
Talk to our engineering team about your specific ML infrastructure needs.