Top 10: GPU Platforms for Deep Learning

GPU platforms are improving deep learning, making the platforms essential for enterprises aiming to accelerate innovation and machine learning

The competition to secure computational power for training neural networks has changed cloud infrastructure in ways few predicted five years ago. 

GPU platforms have become a core of deep learning, providing the parallel processing capabilities required to train models on datasets that would take CPUs months to process. 

Hyperscalers now compete with specialised providers, while decentralised marketplaces challenge traditional pricing models. 

Nvidia’s hardware dominates across platforms, though Google’s TPUs and emerging alternatives from AMD signal diversification is coming. 

Enterprise customers prioritise networking architecture and pricing transparency, while  researchers seek flexibility in instance configuration. 

This week, AI Magazine spotlights some of the top GPU platforms for deep learning and why they are at the top.

10. Vast.ai

Jake Cannell, CEO of Vast.ai

Company: Vast.ai

CEO: Jake Cannell

Specialisation: Decentralised GPU cloud marketplace offering prices via a real-time bidding system

This peer-to-peer model aggregates spare capacity from individual operators, creating pricing pressure that undercuts hyperscalers by margins substantial enough to make CFOs take notice. 

Vast.ai’s inventory ranges from consumer RTX cards to H100 clusters, though availability fluctuates based on what the network can provide at any given moment. 

The platform suits hyperparameter searches and development workloads where instance interruption carries minimal cost – but production deployments typically require the consistency that centralised providers guarantee. 

Users bid for resources in real-time, with pricing determined by supply dynamics rather than fixed rate cards, making it ideal for budget-conscious teams.

9. Paperspace (by DigitalOcean)

Paddy Srinivasan, CEO of DigitalOcean

Company: DigitalOcean (Paperspace)

CEO: Paddy Srinivasan (CEO of DigitalOcean)

Specialisation: End-to-end Machine Learning (ML) platform (Gradient) simplifying the building, training and deployment lifecycle

DigitalOcean acquired Paperspace to extend beyond its developer-focused infrastructure business into ML operations – a move that made sense given where the market was heading. 

The Gradient platform handles versioning and experiment tracking alongside GPU provisioning, targeting teams without dedicated MLOps engineering. 

H100 and A100 instances support production training, while the platform’s templates reduce the configuration overhead that typically eats into actual research time.

Paperspace competes on workflow integration rather than raw compute density, positioning itself between hyperscaler complexity and bare-metal providers. 

The acquisition brought ML capabilities to DigitalOcean’s developer community.

8. RunPod

Zhen Lu, CEO of RunPod | Credit: RunPod

Company: RunPod

CEO: Zhen Lu

Specialisation: Developer-focused GPU cloud marketplace with a range of hardware

Per-second billing eliminates the cost of idle instances, which is a departure from the hourly minimums at larger providers that can add up quickly. 

As a result, RunPod’s inventory includes consumer gaming cards alongside data centre hardware, creating pricing tiers that span two orders of magnitude. 

The Serverless offering pools community-provided GPUs, similar to Vast.ai’s model but with managed orchestration that removes some of the uncertainty.

Instant provisioning and framework templates appeal to prototyping workflows, though networking infrastructure trails the InfiniBand implementations you’ll find at dedicated AI clouds. 

The flexibility makes it popular among independent researchers and small teams.

7. Lambda Labs

Stephen Balaban, CEO of Lambda Labs

Company: Lambda Labs

CEO: Stephen Balaban

Specialisation: GPU cloud platform and integrated software stack tailored for deep learning and enterprise AI

Lambda’s business model focuses exclusively on AI workloads, which means it avoids the general-purpose positioning of hyperscalers and can optimise accordingly. 

The Lambda Stack pre-installs optimised libraries and drivers, reducing the setup friction that typically delays training jobs by hours or even days. 

Quantum-2 InfiniBand networking supports distributed training across H100 and H200 clusters, matching the interconnect performance of much larger competitors. 

The platform markets itself through transparent pricing published directly on its website, a refreshing contrast with the complex rate cards that characterise AWS and Azure. 

Lambda’s customer base skews towards AI-native companies rather than enterprises migrating existing workloads.

6. IBM Cloud

Arvind Krishna, Chairman and CEO of IBM

Company: IBM Cloud

CEO: Arvind Krishna (Chairman and CEO of IBM)

Specialisation: Integrated GPU offerings with flexible server selection and deep integration with the broader IBM ecosystem

IBM Cloud’s value proposition centres on Watson AI integration rather than raw GPU performance metrics, which tells something about where it sits in this market. 

The platform targets enterprises already committed to IBM’s data architecture, offering Nvidia’s GPU instances that connect directly to existing Watson deployments. 

IBM’s global data centre network provides geographic redundancy, though the GPU variety isn’t available at hyperscalers. 

5. CoreWeave

Michael Intrator, CEO of CoreWeave

Company: CoreWeave

CEO: Michael Intrator

Specialisation: AI Hyperscaler built for intensive machine learning, VFX and batch rendering via Kubernetes

CoreWeave emerged from cryptocurrency mining operations to focus on AI infrastructure, raising substantial venture capital to expand data centre capacity at a pace that’s caught attention across the industry. 

The Kubernetes-native architecture differs from traditional VM-based clouds, which means organisations need infrastructure-as-code familiarity but gain granular orchestration in return. 

OpenAI’s deployment on CoreWeave infrastructure validates the platform’s performance claims in a way that marketing materials never could, though independent benchmarks remain limited. 

4. Oracle Cloud Infrastructure (OCI)

Clay Magouyrk and Mike Sicilia, CEO’s of Oracle

Company: Oracle Cloud Infrastructure (OCI)

CEO: Clay Magouyrk and Mike Sicilia

Specialisation: High-performance, low-cost bare metal and VM GPU instances for AI scale-out

Oracle entered the GPU cloud market later than AWS and Azure, but has expanded capacity through partnerships with Nvidia.

The bare metal option removes hypervisor overhead, delivering performance gains that matter when a company is running multi-day training jobs on frontier models where every percentage point counts. 

OCI’s Superclusters implement RDMA networking with 2.5-microsecond latencies, specifications that approach what a company could expect from on-premises clusters.

The platform now offers Nvidia’s Blackwell GB200 and H200 GPUs alongside AMD’s MI300X accelerators – creating vendor diversity that’s notably absent from many competitors. 

Oracle’s database legacy translates to pricing models familiar to enterprise buyers.

3. Microsoft Azure

Satya Nadella, Chairman & CEO of Microsoft

Company: Microsoft Azure

CEO: Satya Nadella (Chairman & CEO of Microsoft)

Specialisation: Enterprise-grade cloud with high-performance N-Series Virtual Machines and a managed ML Platform

Azure’s N-Series VMs provide H100 and A100 instances with InfiniBand networking, matching the technical specifications of AWS’s P-series infrastructure in ways that matter for distributed training. 

The Azure Machine Learning platform integrates with Microsoft’s enterprise software ecosystem – which is particularly relevant for organisations using Office 365 and Dynamics who want to avoid managing multiple vendor relationships. 

Nvidia’s partnership agreements ensure Azure receives new GPU generations within months of announcement, a cadence that smaller providers simply cannot match given the supply constraints. 

The platform’s geographic footprint spans more regions than any competitor except AWS, addressing data residency requirements in regulated industries where this isn’t optional.

2. Amazon Web Services (AWS)

Matt Garman, CEO of AWS

Company: AWS

CEO: Matt Garman (CEO of AWS)

Specialisation: Cloud infrastructure offering a range of EC2 GPU instances

AWS commands the largest market share in cloud computing, which translates to GPU availability across more regions and availability zones than competitors can currently match. 

The P4d instances feature A100 GPUs with 400 Gbps networking, while P5 instances deploy H100 hardware for training jobs requiring thousands of GPUs working in concert. 

EC2 UltraClusters provide dedicated networking fabric for distributed training, reducing the communication overhead that typically limits scaling efficiency once you pass a certain threshold. 

The Deep Learning AMI packages optimise frameworks and drivers, though configuration complexity still exceeds what you’ll find at platforms built exclusively for AI workloads.

The breadth of adjacent services, from S3 storage to SageMaker’s managed ML tools, creates the kind of vendor lock-in that competitors struggle to overcome despite their best efforts. 

1. Google Cloud Platform (GCP)

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Company: Google Cloud

CEO: Thomas Kurian (CEO of Google Cloud)

Specialisation: Offers a blend of Nvidia’s GPUs and proprietary Tensor Processing Units (TPUs) for AI workloads

GCP’s TPU offering is the platform’s technical differentiation, providing hardware optimised specifically for TensorFlow and JAX frameworks that dominate research publications. 

The v4 and v5e TPU generations deliver performance metrics that exceed GPU equivalents for certain model architectures, particularly transformers, which matters given where the field has moved. 

Nvidia’s GPU availability includes H100, A100 and L4 instances through A3 and G2 machine series, maintaining equality with AWS and Azure on the GPU front – while offering something neither can match on the TPU side.

Furthermore, Google’s internal use of TPUs for products including Search and Translate provides operational learnings that inform infrastructure design in ways that pure cloud providers cannot replicate. 

The platform’s networking architecture supports multi-petabit throughput between GPU clusters, with specifications that enable training runs spanning thousands of accelerators – the kind of scale that only a handful of organisations actually need, but that defines the cutting edge of what’s currently possible.