LLM Training - Light Cloud

Train Large Language Models at Scale

Deploy high-performance GPU clusters for pre-training, continued pre-training and fine-tuning of large language models.

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PyTorchDeepSpeedMegatron-LMHugging FaceRayKubernetes

Dedicated infrastructure for foundation model training

Build and train foundation models on dedicated NVIDIA GPUs with ultra-fast networking, distributed storage and optimized infrastructure.

Whether you're training a 7B model or scaling to hundreds of billions of parameters, our GPU cloud provides the performance and flexibility needed for modern AI development.

Built for serious training runs

Dedicated GPU clusters

Non-virtualized hardware reserved for your workload, not shared tenancy.

NVLink & high-speed interconnect

Multi-GPU nodes communicate at full bandwidth for large model training.

Distributed storage

Fast, scalable storage for datasets and checkpoints across nodes.

Kubernetes-ready

Orchestrate training jobs with familiar cloud-native tooling.

PyTorch optimized

Pre-configured images tuned for common training stacks.

JAX & TensorFlow support

Bring the framework your team already knows.

Hourly billing

Pay only for the compute time your runs actually use.

One-click deployment

Go from request to running cluster in hours, not weeks.

RBuilt for serious training runs

Popular

H100

80GB HBM3 · Perfect for large-scale transformer training.

Pre-trainingFine-tuningMulti-GPU

B200

Blackwell generation · Maximum throughput for next-gen foundation models.

Foundation modelsLarge-scale training

L40S

Cost-efficient training and experimentation.

ExperimentationSmaller models

How a training cluster comes together

A typical cluster pairs multiple GPU nodes over NVLink and high-speed interconnect, backed by distributed storage for datasets and checkpoints, and orchestrated through Kubernetes for repeatable, fault-tolerant runs.

Datasets & Storage

Distributed, high-throughput storage

GPU Cluster

NVLink-connected multi-node training

Checkpoints & Registry

Versioned model outputs

Built for every stage of model development

Foundation Model Training

Train Llama, Mistral, Qwen and custom transformer architectures.

Continued Pre-training

Extend existing models using proprietary datasets.

Instruction Tuning

Adapt open-source LLMs for production workloads.

Enterprise AI

Train internal knowledge assistants securely.

Research

Academic and scientific language model development.

Runs on the stack your team already uses

Multi-node NVLink clusters
Hourly or monthly billing
Scale from 1 to 100+ GPUs

PyTorchDeepSpeedMegatron-LMHugging Face TransformersRayAccelerateKubernetesDocker

Built to fit your existing stack

Every deployment ships with API access and console control, so you can automate provisioning or hand it to your engineer of record.

Full API reference and setup guides are shared during onboarding — reach out and we'll get you access.

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                # Launch a training job curl -X POST
                https://api.interhash.ai/v1/llm-training \ -H "Authorization: Bearer
                    $INTERHASH_KEY" \ -H "Content-Type: application/json" \ -d '{"model":
                "llama-3-8b", "gpus": "8xH100", "framework": "pytorch"}'
            

Frequently asked questions

Can I scale a training run mid-way?

Yes. Additional GPUs can be added to an active cluster as your training needs grow.

Do you support multi-node NVLink clusters?

Yes, our H100 and B200 clusters are connected via NVLink and high-speed interconnect for distributed training.

What frameworks are pre-installed?

PyTorch, DeepSpeed, Megatron-LM, Hugging Face Transformers, Ray and Accelerate are available out of the box, with JAX and TensorFlow also supported.

Start your first LLM training cluster in minutes.

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