Top GPU sandboxes for AI agents

Giving an AI agent access to a GPU is harder to implement safely than it sounds. The accelerator must sit inside an isolated environment with a lifecycle and security boundary suited to untrusted or agent-generated code.

This guide compares four platforms: Northflank, Modal, Hugging Face, and NVIDIA OpenShell, to help you find the right GPU sandbox for your AI agent workload.

• Northflank sandboxes suit enterprise production platforms that need governed, persistent or ephemeral GPU environments with access to L4, A100, H100, H200, and more, alongside application infrastructure and self-serve BYOC.
• Modal Sandboxes suit teams that want a managed serverless platform and a broad choice of accelerators, from T4 through B300.
• Hugging Face Sandboxes suit teams that want dedicated VM-based GPU environments integrated with Hugging Face Jobs, images, and hardware flavors.
• NVIDIA OpenShell is an alpha-stage, self-managed runtime for teams experimenting with policy-controlled agent sandboxes on Docker, Podman, Kubernetes, or VMs.

A GPU sandbox is an isolated runtime in which the workload can access an accelerator directly. A platform can offer CPU sandboxes and a separate GPU-backed inference API without supporting GPU execution inside the sandbox itself.

The direct model is useful for CUDA code, local models, image generation, reinforcement learning, or user-submitted training. An agent that only calls a hosted model endpoint may not need a GPU sandbox.

This roundup includes only products whose current first-party material connects GPU resources directly to the sandbox abstraction.

Start with the workload, then compare five practical dimensions:

1. GPU allocation: Check the models, GPU count, and whether hardware is dedicated or shared.
2. Isolation: Identify the microVM, userspace kernel, container, or other boundary. GPU attachment can change which path is available.
3. Lifecycle: Decide whether the agent needs a disposable environment, durable files, paused memory, or an attached volume.
4. Deployment: Choose between a managed service, your own cloud or VPC, or self-hosted software.
5. Platform fit: Decide whether the sandbox must sit beside inference, training, APIs, workers, and databases.

Startup figures are not comparable without the same image, region, GPU, concurrency, and readiness definition. Test your workload before making latency a deciding factor.

The table highlights the architectural and operational differences that affect which platform fits a given GPU workload.

The best options that meet the direct-GPU inclusion test are Northflank sandboxes, Modal Sandboxes, Hugging Face Sandboxes, and NVIDIA OpenShell. Each one makes the GPU available inside its sandbox runtime, but they differ in isolation, persistence, deployment, and maturity.

Northflank sandboxes fit teams whose agent runtime is part of a larger production system. The same platform runs APIs, workers, databases, inference, training, CI/CD, and observability.

• GPUs and lifecycle: GPU sandboxes on Northflank support L4, A100 40 GB and 80 GB, H100, H200, and more. Availability depends on the cloud provider, region, and cluster capacity. Sandboxes can be ephemeral or use attached volumes, with no forced session limit.
• Isolation: Northflank can use Kata Containers with GPU passthrough where nested virtualization is available, or gVisor where it is not.
• Deployment: Northflank sandboxes can run on Northflank’s managed cloud or through self-serve BYOC on AWS, GCP, Azure, Oracle, CoreWeave, on-premises, and existing Kubernetes. With BYOC, workloads and data can remain inside the customer’s cloud boundary.
• Governance: Northflank provides SAML or OIDC SSO, directory sync, RBAC, API roles, and audit logs. It is SOC 2 Type 2 and HIPAA compliant, with BAAs under Enterprise contracts.
• Operations: The Northflank sandbox platform includes network controls, autoscaling, observability, CI/CD, cost tracking, persistent storage, object storage, and managed databases.
• Agent-native operations: Northflank Skills lets agents in Claude Code, Codex, Cursor, OpenCode, and Windsurf deploy and operate services, databases, sandboxes, GPU workloads, previews, and release workflows through the API and CLI.
• Startup performance: ComputeSDK measured Northflank sandboxes at a 67 ms median sequential startup and 198 ms under concurrent burst load, with 100% success in both tests on 24 July 2026. These results are for general sandbox startup, not specifically GPU-backed sandboxes.

Modal Sandboxes fit teams prioritising accelerator choice and managed operation over cloud ownership. The wider platform also handles Functions, inference, training, notebooks, and batch workloads.

• GPUs: T4, L4, A10, L40S, A100 variants, RTX PRO 6000, H100, H200, B200, and B300.
• Isolation: Modal compute uses gVisor.
• Lifecycle: Sandboxes default to five minutes and can run for up to 24 hours.
• Persistence: Volumes and snapshot mechanisms preserve different forms of state.
• Platform fit: Modal connects sandbox execution with managed inference, batch, and training workloads.

Hugging Face Sandboxes are isolated cloud machines built on Hugging Face Jobs. A dedicated sandbox maps one Job to one full VM and can use a GPU inside that VM.

• GPU access: Dedicated Sandboxes can select GPU-backed hardware; the official guide demonstrates an NVIDIA A10G.
• Isolation: One dedicated Sandbox maps to one full VM.
• Tooling: Sandboxes accept Docker images, run commands and background processes, transfer files, proxy services, and reconnect by ID.
• Lifecycle: The idle timeout is configurable, but the underlying Job has a fixed 24-hour maximum.
• Pooling: SandboxPool packs environments onto shared hosts but is CPU-only. GPU workloads use dedicated Sandboxes.

NVIDIA OpenShell takes a different approach from the hosted products above. It is a runtime for creating agent sandboxes against Docker, Podman, Kubernetes, or MicroVM-backed compute that your team operates.

• GPU access: The CLI accepts a GPU count and maps the request through the active compute driver.
• Drivers: Kubernetes uses NVIDIA GPU resources, Docker and Podman use CDI devices, and Linux GPU-backed MicroVMs use QEMU.
• Policy: An in-sandbox supervisor applies controls for process identity, files, network egress, and credentials.
• Deployment: The team supplies and operates the underlying infrastructure.
• Maturity: NVIDIA labels OpenShell alpha software and advises against production use.

GPU sandbox pricing is not one directly comparable hourly rate. As of 29 July 2026:

• Northflank: Published GPU plans combine GPU, CPU, and RAM. Current examples include $0.80/hour for L4 24GB, $1.76/hour for A100 80 GB, and $2.74/hour for H100 80GB.
• Modal: GPU Sandboxes use standard Modal GPU rates, while Sandbox CPU and memory are billed separately at Sandbox-specific rates.
• Hugging Face: Dedicated Sandboxes inherit Jobs pricing, billed by the minute while starting or running. An A10G configuration is currently $1.00/hour.
• NVIDIA OpenShell: There is no hosted compute rate; cost depends on the GPU infrastructure the team supplies.

Choose Northflank sandboxes when you are building a production agent platform and need GPU execution, persistence, APIs, workers, databases, and deployment into your own cloud from one control plane.

Choose Modal Sandboxes when a managed service and a wide accelerator range are the main requirements, especially when your sandboxes sit close to serverless inference, training, or batch jobs.

Choose Hugging Face Sandboxes for dedicated VM-based GPU experiments that use Hugging Face images and hardware flavors and can complete within 24 hours.

Choose NVIDIA OpenShell when you are testing policy-controlled sandboxes on infrastructure you operate and can accept alpha-stage software.

Validate the shortlist with the same image, model, dataset, region, network path, and readiness check. Measure latency, failures, persistence, and total cost.

It is an isolated environment that gives an agent's code direct GPU access while limiting its effect on the host and other workloads. Northflank sandboxes, for example, support GPU-backed execution for inference, training, and compute-intensive agent tasks.

Use one for local inference, image generation, CUDA compilation, reinforcement learning, or user-submitted training. If an agent only calls an external model API, a CPU sandbox is usually enough; Northflank lets teams run both CPU and GPU sandboxes on the same platform.

No. “GPU-enabled” describes resource access, not boundary strength. Review isolation, GPU attachment, tenancy, network policy, credentials, and deployment together. Northflank can use Kata Containers with GPU passthrough where nested virtualization is available, or gVisor where it is not.

Use ephemeral environments for independent tasks. Choose persistence when agents need model weights, repositories, checkpoints, or outputs across sessions. Northflank supports both ephemeral GPU sandboxes and attached volumes without a forced session limit.

Yes, when the platform supports customer-cloud deployment. Northflank's self-serve BYOC runs GPU sandboxes in your VPC with control over regions, networking, IAM, data residency, and cloud billing.

• GPU sandboxes: isolation models and platform support: Understand GPU passthrough, nested virtualization, gVisor, and current platform support.
• How to sandbox AI agents: Review execution isolation, networking, credentials, and lifecycle design for agent workloads.
• Persistent sandboxes: Learn when agent environments need files or processes to survive between sessions.
• Best BYOC sandbox platforms: Compare sandbox deployment inside your own cloud or VPC.
• Best platforms for high-concurrency sandbox environments: Evaluate orchestration and fleet behavior when agents create many environments at once.
• AI sandbox pricing comparison: Model CPU, memory, storage, idle time, and deployment costs across sandbox architectures.