Best Cloud Platforms for Training Large Language Models in 2026

Your choice of cloud provider directly impacts training costs, iteration speed, and how much time you spend fighting infrastructure instead of shipping models.

This guide evaluates 15 platforms – hyperscalers and GPU-focused neoclouds – on multi-node cluster support, interconnect quality, H100 pricing, and operational overhead.

1. Saturn Cloud

Best for: Cost-effective multi-node LLM training with integrated MLOps tooling

Overview: Saturn Cloud offers some of the most affordable on-demand H100/H200 access, making it practical for teams running extended training jobs without exceeding their budgets. The platform handles the infrastructure complexity of multi-node GPU clusters so teams can focus on model development.

Key Features:

• Affordable H100 GPU access at $2.95/hr
• Multi-node cluster support for distributed training
• Pre-configured environments with popular LLM frameworks (PyTorch, DeepSpeed, FSDP)
• Integrated experiment tracking and model versioning

Why Choose Saturn Cloud? For teams training or fine-tuning LLMs who need predictable costs and don’t want to manage Kubernetes clusters or cloud networking themselves. The multi-cloud deployment option also means you’re not locked into a single provider’s GPU availability.

2. Nebius

Best for: Large-scale LLM training with optimized cluster configurations

Overview: Nebius provides AI-native infrastructure with pre-optimized GPU clusters designed for distributed training workloads. Their configurations are designed specifically for the communication patterns required by LLM training.

Key Features:

• Pre-configured clusters optimized for distributed training
• High-bandwidth interconnects between nodes
• Managed services for the full ML workflow
• Infrastructure designed around AI workload requirements

Why Choose Nebius? Teams running large pre-training jobs or extensive fine-tuning campaigns will benefit from Nebius’s infrastructure. Optimized networking reduces communication bottlenecks that slow down distributed LLM training.

3. CoreWeave

Best for: Kubernetes-native LLM training at scale

Overview: CoreWeave has built its infrastructure specifically for GPU workloads, with Kubernetes orchestration that handles the complexity of multi-node training jobs. The platform offers access to large H100 clusters with the networking required for efficient gradient synchronization.

Key Features:

• Large H100 and H200 cluster availability
• InfiniBand interconnects for low-latency communication
• Kubernetes-native job scheduling
• Flexible resource allocation for varying training scales

Why Choose CoreWeave? Teams with Kubernetes expertise who want fine-grained control over their training infrastructure. CoreWeave’s GPU availability and networking make it suitable for serious pre-training runs.

4. Lambda Labs

Best for: ML research teams needing straightforward GPU cluster access

Overview: Lambda Labs offers both cloud instances and on-premise hardware optimized for deep learning. Their cloud platform provides access to multi-GPU instances with configurations tuned for training workloads.

Key Features:

• H100 instances with NVLink for fast GPU-to-GPU communication
• Pre-installed ML frameworks and libraries
• Simple API for programmatic instance management
• Reserved capacity options for longer training runs

Why Choose Lambda Labs? Research teams and startups that want powerful GPU access without navigating complex cloud configurations. Lambda’s focus on ML means less time spent on infrastructure setup.

5. Crusoe

Best for: Sustainable LLM training with carbon-conscious infrastructure

Overview: Crusoe powers its GPU infrastructure with stranded or renewable energy, offering a lower-carbon option for compute-intensive training jobs. The platform provides access to current-generation NVIDIA GPUs with configurations suitable for distributed training.

Key Features:

• H100 GPU clusters powered by renewable energy
• Competitive pricing from alternative energy sourcing
• Configurations optimized for large-scale training
• Lower carbon footprint for extended training runs

Why Choose Crusoe? Organizations tracking their carbon footprint will find Crusoe compelling for LLM training. Multi-week training runs consume significant energy, and Crusoe offers a way to reduce environmental impact without sacrificing performance.

6. Voltage Park

Best for: Large-scale distributed training with advanced networking

Overview: Voltage Park focuses on high-performance GPU infrastructure with the networking capabilities required for efficient distributed LLM training. The platform is designed for workloads that span many nodes.

Key Features:

• Infrastructure optimized for large-scale distributed training
• Low-latency interconnects for gradient synchronization
• Tools for managing multi-node training jobs
• Flexible resource allocation

Why Choose Voltage Park? Teams running training jobs across dozens or hundreds of GPUs will benefit from Voltage Park’s networking infrastructure. The platform is built for the communication patterns that large LLM training demands.

7. Amazon Web Services (AWS)

Best for: Enterprise teams needing managed services alongside training infrastructure

Overview: AWS offers P5 instances with H100 GPUs and UltraCluster configurations for large-scale training. SageMaker provides managed training jobs with distributed training support, though costs add up quickly for extended runs.

Key Features:

• P5 instances with H100 GPUs and EFA networking
• SageMaker distributed training with built-in parallelism strategies
• Trainium chips as a cost-optimization option
• Integration with S3 for dataset and checkpoint storage

Why Choose AWS? Enterprise teams already invested in AWS infrastructure who need the compliance, security, and service integration that comes with a major cloud provider. Budget-conscious teams should carefully model costs before committing to long training runs.

8. Google Cloud Platform (GCP)

Best for: Teams wanting managed training infrastructure with TPU options

Overview: GCP provides A3 instances with H100 GPUs and offers TPU pods as an alternative for certain training workloads. Vertex AI includes managed training capabilities and supports distributed jobs.

Key Features:

• A3 instances with H100 GPUs
• TPU v5p pods for alternative training hardware
• Vertex AI managed training with distributed support
• Integration with BigQuery for data pipelines

Why Choose GCP? Teams interested in exploring TPU-based training or those already using GCP’s data services. The managed training options reduce operational overhead, though pricing requires careful attention.

9. Microsoft Azure

Best for: Enterprise organizations in the Microsoft ecosystem

Overview: Azure provides ND-series VMs with H100 GPUs and InfiniBand networking for distributed training. Azure ML offers managed training with support for popular distributed training frameworks.

Key Features:

• ND H100 v5 instances with InfiniBand
• Azure ML managed training jobs
• Integration with Azure storage and data services
• Enterprise security and compliance features

Why Choose Azure? Organizations using Microsoft tools and services who need enterprise-grade security and compliance for their training infrastructure. The ecosystem integration simplifies data pipelines for teams already on Azure.

10. TensorWave

Best for: LLM training on AMD GPU infrastructure

Overview: TensorWave provides bare-metal infrastructure powered by AMD Instinct MI300X accelerators, offering an alternative to NVIDIA-based training. The platform focuses on high-performance AI workloads with optimized configurations.

Key Features:

• AMD Instinct MI300X accelerators with high memory bandwidth
• Bare-metal infrastructure for maximum performance
• Configurations optimized for large model training
• Competitive pricing compared to H100 alternatives

Why Choose TensorWave? Teams open to AMD hardware who want to avoid NVIDIA supply constraints or explore cost alternatives. MI300X’s large memory capacity can be advantageous for training larger batch sizes.

11. Oracle Cloud Infrastructure (OCI)

Best for: High-performance bare-metal GPU access with competitive pricing

Overview: OCI offers bare-metal GPU instances with dedicated hardware, providing consistent performance without noisy neighbor issues. The platform’s cluster networking supports distributed training workloads.

Key Features:

• Bare-metal GPU shapes with dedicated hardware
• RDMA cluster networking for distributed training
• Competitive pricing compared to other hyperscalers
• Flexible configurations for different training scales

Why Choose OCI? Teams wanting bare-metal performance with hyperscaler reliability at better price points than AWS or GCP. The dedicated hardware eliminates virtualization overhead during training.

12. NScale

Best for: Flexible GPU scaling for varying training workloads

Overview: NScale provides cloud GPU resources that can scale based on training requirements. The platform supports the frameworks and tools commonly used for LLM development.

Key Features:

• Scalable GPU resource allocation
• Support for major ML frameworks
• Configurations for distributed training
• Transparent pricing models

Why Choose NScale? Teams with variable training needs who want flexibility in resource allocation without long-term commitments.

13. GMI Cloud

Best for: Research teams with complex computational requirements

Overview: GMI Cloud provides infrastructure for advanced ML and scientific computing, with GPU resources suitable for demanding training workloads.

Key Features:

• High-end GPU resources for intensive training
• Support for complex distributed configurations
• Tools for monitoring training efficiency
• Flexible infrastructure options

Why Choose GMI Cloud? Research organizations running experimental training configurations or working on novel architectures.

14. Digital Ocean Paperspace

Best for: Smaller-scale fine-tuning and experimentation

Overview: Paperspace offers accessible GPU computing for ML development. While not designed for massive pre-training runs, it works well for fine-tuning workflows and model experimentation.

Key Features:

• Gradient platform for experiment management
• Various NVIDIA GPU options
• Notebook environments for development
• Straightforward pricing

Why Choose Paperspace? Teams focused on fine-tuning existing models rather than pre-training from scratch. The platform’s simplicity makes it good for experimentation and smaller-scale training.

15. Vultr

Best for: Budget-conscious fine-tuning workloads

Overview: Vultr provides straightforward GPU cloud access at competitive prices. The platform suits smaller training jobs and fine-tuning workflows where massive scale isn’t required.

Key Features:

• Simple instance provisioning
• Transparent hourly and monthly pricing
• Multiple data center locations
• Basic GPU configurations for ML workloads

Why Choose Vultr? Teams with modest compute requirements who prioritize simplicity and cost over advanced features.

Choosing the Right Platform for LLM Training

Selecting infrastructure for LLM training comes down to a few key factors:

Training scale: Pre-training billion-parameter models requires different infrastructure than fine-tuning. For large-scale pre-training, prioritize providers with proven multi-node clusters and high-speed interconnects (CoreWeave, Nebius, Voltage Park). For fine-tuning, more options become viable.

Budget: Extended training runs get expensive quickly. Saturn Cloud and Crusoe offer competitive H100 pricing. TensorWave’s AMD infrastructure provides an alternative. Hyperscalers (AWS, GCP, Azure) typically cost more but offer broader service integration.

Operational complexity: Managed platforms reduce the infrastructure burden but may limit flexibility. Teams comfortable with Kubernetes have more options; those wanting simplicity should look at platforms that abstract away cluster management.

Hardware preferences: Most providers focus on NVIDIA GPUs, but TensorWave offers AMD alternatives, and GCP provides TPU options. Hardware availability varies—specialized providers often have better GPU availability than hyperscalers during supply constraints.

Saturn Cloud stands out for teams seeking affordable H100 access, integrated MLOps tools, and the flexibility to deploy across multiple clouds. If you’re evaluating options for your next training project, visit Saturn Cloud to learn more.