GPU Cloud & Neocloud Providers

You've got racks of GPUs and Kubernetes provisioning the infrastructure. Your tenants need more than raw compute. Helix is a ready-made AI agent platform you can deploy and offer as a managed service.

The GPU utilisation problem

You've invested heavily in GPU infrastructure. You've got the hardware, the networking, the Kubernetes layer, the provisioning automation. Your customers can spin up GPU instances and run inference workloads.

But here's the conversation that keeps happening:

"We've got the GPUs. What do we actually run on them?"

Raw compute is a commodity. Every hyperscaler offers it. Every neocloud offers it. The differentiation isn't in the hardware — it's in what you help your customers do with it.

Most GPU cloud customers today are running inference endpoints or fine-tuning jobs. That's valuable, but it's a narrow slice of what GPU infrastructure can do. The next wave of GPU demand is coming from AI agent workloads — autonomous agents that need GPU-accelerated desktops, real-time streaming, and isolated sandboxed environments to do real work.

This is a different kind of workload. It's not batch inference. It's not training. It's dozens of interactive, long-running agent sessions that need GPU for rendering, model inference, and real-time video streaming — simultaneously. It's the kind of workload that drives sustained GPU utilisation, not spiky batch jobs.

What your tenants are asking for

Your enterprise customers are starting to ask for more than compute:

"We want to offer AI agent desktops to our developers" — Not just API access to a model, but a full sandboxed environment where an AI agent can write code, browse the web, test applications, and produce real output. Each agent needs its own isolated desktop with GPU-accelerated rendering.

"We need multi-tenancy with proper isolation" — Different teams, different projects, different security boundaries. Agents for one tenant can't see another tenant's code, credentials, or data.

"We want to resell this as a managed service" — They don't want to build an AI agent platform from scratch. They want to deploy something that works, brand it, and offer it to their own customers.

"We need to show our investors GPU utilisation metrics" — AI agent workloads drive real, sustained GPU utilisation. Not the spiky utilisation of batch jobs, but the kind of steady-state consumption that makes infrastructure economics work.

How Helix fits your stack

Helix deploys on Kubernetes — the same Kubernetes you're already running. It consumes GPUs for agent desktop rendering, model inference, and real-time video streaming. It's the application layer that turns your GPU infrastructure into a differentiated product.

Deploys on your infrastructure — Helix runs as a standard Kubernetes deployment. Helm chart, standard resource requests, standard GPU scheduling. No special hardware requirements beyond the GPUs you already have.

Drives GPU utilisation — Each agent desktop uses GPU for rendering. Real-time video streaming to users is hardware-accelerated. If you're also serving model inference locally, that's additional GPU demand. 15+ concurrent agent desktops per node, each consuming GPU resources.

Multi-tenant by design — RBAC, project-level isolation, token metering per team and project. Your tenants get their own isolated environments. You get visibility into resource consumption across all tenants.

White-label ready — Helix is the engine. You can offer it as part of your platform's AI services tier. Your customers get agent desktops, fleet orchestration, and multiplayer collaboration. You get a high-value product offering that drives GPU consumption.

SOC 2 Type II and ISO 27001 certified — Independently audited security controls. When your enterprise tenants ask about compliance, you have the certifications ready.

The economics

GPU hours spent idle are revenue left on the table. AI agent workloads are uniquely good at driving sustained utilisation because they're interactive and long-running — a developer might have 5–10 agents running all day, each consuming GPU for desktop rendering and inference.

Compare this to the typical GPU workload pattern:

Agent workloads turn GPU hours into a per-seat SaaS product. Your customers pay per developer per month. You know exactly how much GPU each seat consumes. The revenue is predictable, the utilisation is sustained, and the value proposition is clear.

Partnership model

We're looking for GPU cloud and neocloud partners who want to offer AI agent desktops as part of their platform. The model is straightforward:

• You provide the GPU infrastructure and Kubernetes layer
• Helix provides the AI agent platform that runs on top
• Your customers get a differentiated product — not just raw compute, but a ready-to-use AI agent platform
• You drive GPU utilisation and per-seat revenue

We can support joint go-to-market, co-branded deployments, and technical integration with your existing provisioning and billing systems.

Get started

Technical evaluation — Deploy Helix on your Kubernetes cluster and test the agent desktop experience. We'll help you understand the GPU resource profile and plan capacity. Talk to us →

Partnership discussion — If you're a GPU cloud or neocloud provider and want to explore offering AI agent desktops as a managed service, we'd like to talk. Contact our partnerships team →