GPU Infrastructure for Training AI Models
From fine-tuning to training from scratch. Multi-GPU clusters, automated checkpointing, and monitoring. We provide the compute and manage the environment.
Training Models Is Hard. Infrastructure Shouldn\'t Be.
Training runs are expensive, time-consuming, and unforgiving. A hardware failure on day 5 of a 7-day run without proper checkpointing? Start over.
We handle the infrastructure complexity so you can focus on what matters: your model, your data, your experiments.
Training-Specific Challenges
- Training runs take days or weeks—infrastructure must be reliable
- Multi-GPU and multi-node coordination is complex to set up
- Data pipeline bottlenecks kill GPU utilization
- Cost management across long training runs is hard
- Checkpointing and recovery from failures needs to work
Training Infrastructure That Works
Everything you need for reliable, efficient model training.
Right-Sized GPU Selection
We help you pick GPUs that match your training needs. No overpaying for H100s when A100s will do.
Multi-Node Training
Distributed training across GPU clusters with proper networking, storage, and framework configuration.
Data Pipeline Optimization
High-throughput data loading so GPUs never sit idle waiting for data.
Automated Checkpointing
Model checkpointing configured properly. If something fails, resume from the last checkpoint.
Cost Monitoring
Track spend per training run. Know exactly what each experiment costs.
Environment Management
Reproducible training environments. Same results when you run it again.
High-Speed Storage
Fast access to training datasets. NVMe for active data, object storage for archives.
24/7 Monitoring
We watch the hardware so you watch the metrics. Alerts if something goes wrong.
Pick the Right GPU for Your Workload
Honest recommendations. We don\'t upsell.
| Workload | Recommended GPU | Why | |
|---|---|---|---|
| Fine-tuning (< 7B params) | A100 40GB | Cost-effective, sufficient memory | Details → |
| Fine-tuning (7B-30B params) | A100 80GB / H100 | More memory, faster | Details → |
| Training from scratch | H100 / H200 | Maximum performance, latest gen | Details → |
| Multi-modal training | H100 / H200 | High bandwidth for mixed workloads | Details → |
| Budget-conscious training | A100 40GB | Best price/performance ratio | Details → |
Training Workloads We Support
Fine-Tuning Open-Source Models
Adapting Llama, Mistral, or other open models to your specific use case. We set up the infrastructure and help you pick the right GPU configuration.
Llama 3, Mistral, Mixtral, PhiTraining From Scratch
Building custom models from the ground up. Multi-node clusters with proper distributed training, checkpointing, and monitoring.
Custom architectures, research modelsComputer Vision Training
Large image and video datasets need serious storage throughput. We architect systems where data loading never bottlenecks training.
CNNs, ViTs, detection modelsNLP Pre-Training
Pre-training language models requires massive compute and long runs. Reliable infrastructure with proper checkpointing is critical.
BERT variants, GPT-style, encodersReinforcement Learning
GPU-accelerated simulation environments and training. Complex infrastructure needs expert setup.
RL agents, robotics, game AIFrom Conversation to Training
Understand Your Workload
Model size, dataset size, training timeline, budget. We need to understand what you\'re building.
Design the Setup
GPU selection, cluster size, storage architecture, networking. We design infrastructure that fits.
Build and Configure
We provision hardware, install frameworks, configure distributed training, set up monitoring.
Hand Off and Support
You get a ready-to-use training environment. We monitor infrastructure and handle issues.
Common Questions
How do you help with distributed training?
We configure multi-node clusters with proper networking (InfiniBand or high-speed Ethernet), shared storage, and distributed training frameworks like PyTorch DDP, DeepSpeed, or FSDP. We test the setup before handing it off.
What happens if a training run fails mid-way?
We configure automated checkpointing so you can resume from the last saved state. We also monitor hardware health to catch issues before they cause failures.
How do I know which GPU to choose?
Tell us about your model size, batch size, and budget constraints. We'll recommend the right GPU—and we don't upsell. If A100s will do the job, we won't push H100s.
Can you help with data pipeline optimization?
Yes. We architect storage for high throughput and help configure data loading to keep GPUs busy. If your GPUs are sitting idle waiting for data, that's wasted money.
Do you provide pre-configured ML environments?
Yes. PyTorch, TensorFlow, JAX, and common libraries are pre-installed and tested. CUDA and cuDNN are configured. You can start training immediately.
How does cost tracking work?
We can set up tracking so you know the GPU cost of each training run. Useful for comparing experiments and budgeting compute spend.
Continue Your AI Journey
Tell Us About Your Model
Model architecture, dataset size, training timeline. We\'ll help you design infrastructure that gets your model trained—reliably and on budget.