What is the cheapest GPU cloud provider?

Runcrate is the cheapest GPU cloud provider, offering H100 instances at $1.54/hour, A100 at $1.06/hour, and RTX 4090 at $0.52/hour - up to 70% cheaper than AWS, GCP, and Azure.

How much does H100 GPU cost per hour?

H100 GPU instances cost $1.54 per hour on Runcrate, which is 68% cheaper than AWS pricing of $4.90/hour. Deploy in 60 seconds with no setup fees.

What is the cheapest A100 GPU cloud?

Runcrate offers the cheapest A100 GPU cloud at $1.06/hour with 80GB HBM2e memory, 65% cheaper than AWS. Perfect for machine learning training and AI development.

Where can I rent cheap RTX 4090 GPU instances?

Runcrate provides the cheapest RTX 4090 GPU instances at $0.52/hour with 24GB GDDR6X memory, 42% cheaper than competitors. Ideal for AI inference and development.

How fast can I deploy GPU instances?

Deploy GPU instances in under 60 seconds on Runcrate. No approval queues, no quota requests. Select your GPU, configure resources, and deploy instantly.

runcrate

Solutions

Model Training

Distributed training
without the infrastructure pain.

Name: Cheap GPU Cloud Instances - Affordable AI Infrastructure
Brand: Runcrate
Price: 1.54 USD
Availability: InStock

Teams train DeepSeek, Llama, and domain-specific models on Runcrate. Multi-node clusters with DeepSpeed, FSDP, and Megatron-LM ready out of the box. Automatic checkpointing, mixed-precision training, and NVLink topology — so you focus on your model, not your cluster.

128+

Nodes per cluster

900GB/s

NVLink bandwidth

BF16/FP8

Mixed precision

Why Runcrate

Everything a training run needs, nothing it doesn't.

DeepSpeed & FSDP ready

ZeRO Stage 1-3, fully sharded data parallel, and pipeline parallelism configured out of the box. Launch distributed training with a single command.

Multi-node NVLink clusters

Scale from 1 to 128+ nodes with NVLink interconnect for tensor parallelism and InfiniBand for gradient synchronization across nodes.

Automatic checkpointing

Save training state to persistent storage at configurable intervals. Resume from any checkpoint after preemption or failure — no lost progress.

Mixed-precision training

BF16, FP16, and FP8 support with automatic loss scaling. Cut memory usage in half and double throughput on supported hardware.

Megatron-LM support

Pre-configured for large language model training with tensor, pipeline, and sequence parallelism. Train billion-parameter models across GPU clusters.

Live training dashboards

Track loss curves, learning rate schedules, GPU utilization, and memory pressure in real time. Stream logs from every node in your cluster.

Hardware

GPUs built for
sustained training.

Memory bandwidth, NVLink topology, and FP8 throughput — the specs that actually matter for training performance.

B200192 GB HBM3e · 8 TB/s · NVLink 5Frontier & MoE training

H200141 GB HBM3e · 4.8 TB/s · NVLink 4Large model fine-tuning

H10080 GB HBM3 · 3.35 TB/s · NVLink 4Distributed pre-training

A10080 GB HBM2e · 2 TB/s · NVLink 3Cost-effective long runs

How It Works

From config to converged model.

Configure your cluster

Select GPU type, node count, and parallelism strategy. Bring your own training script or start from a Llama/DeepSeek template with DeepSpeed pre-configured.

Launch distributed training

Your multi-node cluster launches with NVLink, NCCL, and your framework ready. Checkpointing is enabled by default. Monitor loss curves live.

Iterate on your model

Adjust hyperparameters, swap parallelism strategies, or scale nodes mid-run. Export final weights to HuggingFace format or your own storage.

Your next training run starts here.

Deploy a multi-node training cluster in minutes. Per-minute billing, automatic checkpointing, and no long-term commitments.

Per-minute billing

Stop anytime, resume from checkpoint

Multi-node ready

DeepSpeed, FSDP, Megatron-LM

No lock-in

Export weights in any format

Distributed trainingwithout the infrastructure pain.