GPU Cloud

Best GPU Clouds Without Quotas or Waitlists in 2026

For AI teams building inference APIs, training large models, or running massive batch jobs, GPU availability is often the bottleneck. Hyperscalers such as AWS and Google Cloud use GPU or accelerator quotas, and some accounts must request quota increases before launching additional accelerators. Quota approval and physical hardware availability are separate constraints, so access friction can vary by account, region, GPU family, and live capacity. A new generation of GPU cloud infrastructure reduces that friction by offering self-service GPU access without the traditional hyperscaler quota-request workflow or a capacity reservation for ordinary deployments. This guide examines seven GPU cloud providers that offer on-demand GPU access in 2026. It starts with Modal, a serverless AI infrastructure platform that combines fast cold starts, automatic scaling, per-second GPU billing, a code-first developer experience, and broad support for inference, training, batch processing, Sandboxes, and Notebooks. For teams that need elastic GPU compute without managing fleets of GPU virtual machines, Modal is the standout choice.

Modal TeamEngineering
September 202618 min read
GPU cloud infrastructure

Key Takeaways

  • Serverless architecture reduces continuously provisioned idle capacity: Modal can scale back to zero and bills GPU compute per second, making it well suited to variable and bursty workloads.
  • Fast cold starts improve iteration speed: Modal is engineered for fast cold starts and faster feedback loops. Its optimized filesystem helps containers come online quickly without letting large images slow startup down, while GPU Memory Snapshots can accelerate initialization-heavy workloads.
  • Fine-grained billing matters for variable workloads: Modal uses per-second GPU pricing. Its serverless economics are especially compelling when demand varies substantially over time because compute can scale with the workload instead of remaining continuously provisioned.
  • Self-service access reduces quota friction: TensorDock explicitly markets no quotas. Modal uses a pooled multi-cloud capacity model that avoids the traditional hyperscaler quota-request workflow for serverless GPU access.
  • Marketplace models aggregate hardware access: Vast.ai, TensorDock, and Spheron aggregate GPU capacity across multiple hosts or providers and expose a range of hardware and deployment models.
  • Modal combines enterprise controls with serverless AI infrastructure: Modal has completed a SOC 2 Type 2 audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA.

1. Modal: Serverless AI Infrastructure with Fast Cold Starts

Modal delivers a serverless GPU platform engineered for AI workloads. Unlike traditional cloud providers that primarily expose GPU-attached virtual machines, Modal provides a code-first execution model in which applications define compute requirements in code and Modal handles containerization, scheduling, scaling, cloud execution, and teardown automatically. Modal supports SDKs and code-defined infrastructure workflows in Python, TypeScript, and Go. Modal Sandboxes can run whatever programming language or runtime a workload requires, making the platform suitable for both model-centric workloads and broader AI application infrastructure.

How Modal Works

Modal's architecture differs fundamentally from VM-based GPU clouds. Applications define the compute they need, while Modal manages the underlying infrastructure:

  • Container-based execution: Modal's execution model packages application code into containers and runs it in the cloud without requiring teams to manage Kubernetes clusters or GPU VM fleets.
  • Automatic scaling: Modal can scale inference workloads to 1,000+ GPUs and back to zero, while batch workloads can fan out across thousands of containers.
  • Fast cold starts: Engineered for fast cold starts and faster feedback loops, with an optimized filesystem that helps containers come online quickly without letting large images slow startup down.
  • Code-first infrastructure: Modal supports SDK-based workflows in Python, TypeScript, and Go, allowing application and infrastructure logic to stay close together.

Key Capabilities

Modal's platform spans the core AI workload lifecycle:

  • Inference: Deploy models with automatic scaling, dynamic batching, and GPU Memory Snapshots for initialization-heavy workloads.
  • Training: Run single-node and multi-node training on B200, H200, and H100 clusters with high-speed private networking and RDMA.
  • Batch Processing: Queue up to 1 million inputs and scale processing across thousands of containers automatically.
  • Sandboxes: Run isolated environments for AI-generated and untrusted code at scale. Sandboxes can execute any language or runtime required by the workload and are well suited to cloud coding agents, preview environments, code execution, file operations, and other execution-heavy agent workflows.
  • Notebooks: Use collaborative GPU-backed notebooks with real-time collaboration, automatic idle shutdown, rapid startup, and GPU usage priced by the second.

GPU Availability

Modal provides serverless access to a broad current GPU catalog through its pooled multi-cloud capacity model, without the conventional hyperscaler quota-request workflow:

Security and Compliance

Modal maintains enterprise-grade security controls:

Best For: Teams building production AI applications that need elastic GPU access without managing GPU fleets. Modal is particularly strong for inference APIs with variable traffic, distributed training, massively parallel batch processing, GPU-backed experimentation, and AI systems that need secure isolated execution.

2. Vast.ai: Decentralized GPU Marketplace

Vast.ai operates a distributed GPU marketplace connecting independent compute providers with buyers. Its marketplace spans consumer and data-center GPU options across multiple hosts and locations.

Marketplace Model

Vast.ai's marketplace model provides access to distributed GPU capacity:

  • Host diversity: Listings include consumer RTX cards and data-center GPUs such as H100, H200, B200, and B300-class hardware.
  • Variable pricing: Rates are determined by marketplace supply and demand.
  • Interruptible instances: Bid-based instances are available alongside on-demand rentals.

Platform Characteristics

  • Broad GPU selection across consumer and enterprise hardware
  • Host-specific storage, networking, location, and reliability characteristics
  • Marketplace information for comparing available offers
  • Secure Cloud capacity through data-center partners
  • Serverless endpoints with scale-to-zero support alongside marketplace instances

Marketplace Characteristics

Vast.ai combines marketplace-style instance access with serverless options. Individual listings can differ in hardware configuration, networking, storage, location, and service characteristics, while Secure Cloud provides a data-center-focused service tier. Best For: Teams that want broad GPU selection, marketplace pricing, interruptible capacity, and access to both instance-based and serverless deployment models.

3. TensorDock: Explicit No-Quota GPU Cloud

TensorDock explicitly markets "No quotas, no commitments" and operates a global GPU marketplace that aggregates capacity from independent hosts and infrastructure partners.

No-Quota Policy

TensorDock's positioning removes the conventional quota-approval step common in larger clouds:

  • Self-service deployments marketed without a quota-request process
  • H100 and other enterprise GPU options, subject to current inventory
  • Pay-as-you-go on-demand access with reserved pricing for longer-term rentals

Platform Features

  • Distributed GPU capacity through partners across many locations
  • Virtual-machine instances supplied through a marketplace of independent hosts
  • Interruptible, bid-based instances alongside on-demand access
  • Pay-as-you-go access and reserved pricing options
  • A stated uptime standard for hosts

Instance Options

TensorDock primarily provides VM-style GPU rental. Users can launch self-service instances and manage them directly, with GPU availability varying by model and location. Best For: Teams seeking quota-free, self-service VM-style GPU access through a globally distributed marketplace, with flexible on-demand and longer-term options.

4. JarvisLabs: Developer-Focused GPU Cloud

JarvisLabs targets developers, researchers, and small teams with GPU cloud services that include conventional VMs, managed GPU Templates, and serverless endpoints.

Key Features

  • Per-minute billing for running GPU resources
  • Pause and resume workflows with separately priced storage
  • H200 and other high-end GPU options
  • Managed GPU Templates with JupyterLab, browser VS Code, and SSH access
  • Serverless endpoints with scale-to-zero worker pools

Developer Experience

JarvisLabs combines VM access with managed development environments and serverless endpoints. Managed Templates support on-demand launches, while traditional VMs provide direct machine control and the serverless product supports variable inference workloads. Best For: Individual researchers and small teams seeking GPU access with managed development environments, per-minute billing, pause and resume workflows, and an optional scale-to-zero serverless path.

5. Oblivus Cloud: Enterprise GPU Infrastructure

Oblivus Cloud provides GPU infrastructure for production workloads, including traditional VM-based services, reserved capacity, dedicated configurations, and marketplace access.

Platform Capabilities

  • H100 and H200 configurations in its cloud catalog with on-demand inventory organized by region
  • On-demand and reserved capacity options
  • KVM-based virtual servers with H100 and H200 configurations, plus API and SSH access
  • GPU Marketplace inventory that changes with available capacity

Enterprise Focus

Oblivus targets organizations that want dedicated or reserved GPU infrastructure with enterprise support and configurable deployments. Its data-center pages list facilities associated with certifications and compliance frameworks including ISO 27001, SOC 2 Type 2, and HIPAA. Best For: Enterprise teams that want a mix of traditional VM infrastructure, reserved or dedicated capacity, and marketplace-style GPU discovery.

6. Thunder Compute: GPU Cloud with Per-Minute Billing

Thunder Compute uses per-minute GPU pricing with baseline storage included and no data-egress charges.

Pricing Model

  • Per-minute billing
  • 100 GB of storage included per GPU, with additional storage billed separately
  • No data-egress fees
  • H100 PCIe availability alongside A100, L40, and RTX A6000 options
  • Student credits for eligible users at U.S. universities

Simplicity Focus

Thunder Compute uses a direct VM-style GPU model. Baseline storage is bundled with GPU instances, while additional storage and snapshots are separately metered. Best For: Teams, researchers, and eligible students seeking per-minute GPU access with included baseline storage and no egress charges.

7. Spheron Network: Aggregated GPU Computing

Spheron operates an aggregated GPU marketplace that provides a single interface across multiple underlying providers, with bare-metal, spot, and on-demand capacity options.

Aggregated Architecture

  • GPU capacity aggregated across multiple providers, including bare-metal options
  • Spot and on-demand instance types
  • H100, H200, B200, A100, L40S, and other GPU options, subject to live supply
  • Root access with VM or bare-metal instance options
  • Per-minute billing

Reliability and Capacity Tiers

Spheron separates interruptible spot capacity from dedicated, non-interruptible on-demand GPU instances. Its dedicated on-demand tier includes a published uptime SLA. Best For: Teams that want aggregated bare-metal GPU capacity, root access, per-minute billing, and a choice between spot instances and dedicated on-demand capacity.

Why Modal Stands Out for GPU Cloud Without Traditional Quota Requests

Serverless GPU Architecture

Modal's advantage is the breadth and integration of its serverless AI platform. It combines scale-to-zero inference, distributed training, massively parallel batch processing, secure Sandboxes, GPU-backed Notebooks, code-defined infrastructure, and per-second compute billing in one platform. This matters for three reasons:

  1. Fine-grained resource allocation: Modal scales GPU resources with workload demand instead of requiring teams to keep a continuously provisioned GPU VM fleet running.
  2. Automatic elasticity: Modal can scale inference workloads to 1,000+ GPUs and back to zero and can launch thousands of containers for large batch jobs without requiring manual instance provisioning.
  3. Code-first infrastructure: Modal supports SDKs and code-defined infrastructure workflows in Python, TypeScript, and Go, while Modal Sandboxes can run any programming language or runtime required by the workload.

Cold Start Performance

Modal is engineered for fast cold starts and faster feedback loops. Its optimized filesystem helps containers come online quickly without letting large images slow startup down. For initialization-heavy GPU workloads, GPU Memory Snapshots can restore initialized runtime state so repeated startup work does not need to be recomputed. Together with Modal's container architecture and image system, this supports responsive scaling for inference, experimentation, and other dynamic AI workloads.

Code-First Developer Experience

Modal uses SDKs and code-defined infrastructure to keep application logic and compute configuration closely connected. Python, TypeScript, and Go are supported for Modal development workflows, and Sandboxes can execute workloads in any programming language or runtime. The Modal introduction describes a development model that avoids requiring teams to manage Kubernetes clusters, GPU VM fleets, or a separate cloud account for normal Modal workloads. This keeps the infrastructure surface area focused on application and model requirements.

Production-Grade at Scale

Modal's customer examples document production AI deployments across startups, scale-ups, and enterprises. Modal also supports agent-specific production use cases, including the Ramp coding agent architecture built on Modal Sandboxes. That production track record is paired with enterprise controls including audit logs, SSO, a completed SOC 2 Type 2 audit, and support for HIPAA-compliant workloads on Enterprise plans via a BAA.

Comprehensive Workload Support

Modal spans the AI development lifecycle through inference, training, batch processing, Sandboxes, and Notebooks. That breadth lets teams run multiple workload types on one code-first platform instead of maintaining separate infrastructure systems for every stage. For teams evaluating GPU clouds that minimize hyperscaler-style quota friction, Modal's combination of serverless architecture, fast cold starts, fine-grained billing, integrated workload primitives, broad GPU access, and enterprise security makes it the clear choice for production AI workloads with variable demand patterns. Get started with Modal's Starter plan to evaluate the platform.

Frequently Asked Questions

What does "no quotas or waitlists" mean for GPU cloud access?

In this guide, "without quotas or waitlists" means a provider generally lets users access self-service GPU inventory without first completing a hyperscaler-style quota approval or sales-gated capacity request for ordinary deployments. It does not imply that every GPU model is continuously available in every region. AWS uses per-account, per-region On-Demand Instance quotas, and Google Cloud applies GPU quotas to projects. Modal avoids the conventional quota-request workflow for its serverless GPU platform through a pooled multi-cloud capacity model. Marketplace providers similarly expose inventory through self-service interfaces rather than the traditional hyperscaler quota-increase process.

How does per-second billing benefit AI and ML development?

Billing granularity affects how closely infrastructure charges track actual runtime. Modal bills listed GPU resources per second, which is useful for short jobs, bursty inference, experiments, and batch workloads that scale up and down frequently. Billing granularity varies across the other providers. JarvisLabs, Thunder Compute, and Spheron use minute-level billing for relevant GPU services, while other providers use different metering models. Modal's per-second serverless model combines fine-grained metering with automatic scaling and scale-to-zero infrastructure.

What types of GPUs are typically available from these providers?

High-end 2026 options across the group include NVIDIA H100, H200, B200, and B300-class hardware. Modal pricing lists B300, B200, H200, H100, RTX PRO 6000, A100, L40S, A10, L4, and T4, while the other providers expose different combinations of data-center, professional, and consumer GPUs. A100s remain widely represented across GPU clouds, while lower-cost inference options can include L40S, L40, L4, RTX 4090, RTX A6000, and similar GPUs. Exact inventory depends on the provider, service model, region, and current supply.

Are there specific use cases where a decentralized GPU cloud is more beneficial?

Distributed and aggregated GPU marketplaces provide hardware diversity, flexible inventory models, and marketplace pricing structures. Vast.ai provides marketplace access alongside serverless inference, TensorDock aggregates VM capacity from independent hosts, and Spheron combines capacity from multiple providers with spot and dedicated on-demand options. Modal takes a different approach by abstracting the underlying infrastructure behind a serverless execution model. That makes Modal especially well suited to teams that prioritize automatic scaling, code-defined infrastructure, per-second billing, and a unified platform for inference, training, batch, Sandboxes, and Notebooks.

How important is security and compliance when choosing a GPU cloud provider?

Security and compliance depend on the workload, service tier, and infrastructure model. Marketplace and aggregated-cloud providers can structure controls differently across hosts, facilities, and service tiers. Modal has completed a SOC 2 Type 2 audit, uses gVisor-based isolation, supports enterprise SSO and audit logs, and supports HIPAA-compliant workloads on Enterprise plans via a BAA. These controls are integrated into the same serverless platform used for inference, training, batch processing, Sandboxes, and Notebooks.

Can I integrate these GPU cloud services with existing MLOps pipelines?

Integration models vary across providers. Modal supports SDKs and code-defined infrastructure workflows in Python, TypeScript, and Go. Modal applications can expose HTTP endpoints, and OIDC authentication can provide short-lived identity tokens for external systems such as AWS, GCP, Azure, Vault, and other compatible services. Modal Sandboxes can run any language or runtime required by the workload, which makes them useful for execution-heavy AI systems that need isolated code execution or file operations. Instance-based and marketplace providers typically expose combinations of APIs, CLIs, SDKs, and SSH, while Modal provides a more integrated serverless orchestration model for teams that want infrastructure to scale directly with application demand.

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