Bring GPUs when and where you need them
We pool world’s GPU supply in a virtual cluster that transparently plugs into your existing infrastructure. No migration, unlimited elasticity.
Our promise
Boston Univ.
Duke
PUC-Behring
BITS Pilani
CMU
Arizona State
DevRev
Collinear AI
Red HatGPU-Bridge
A new architecture pattern that separates where your software runs from where your GPUs are. Your existing stack stays exactly as-is. It need not migrate to another cloud or become multi-cloud.
GPU-Bridge makes network-connected GPUs work as if local. No software reinstall, no data copy, no changes to IAM and security roles. Your workload runs in the full context of the machine you launched it from, including its filesystem, network, devices, IPC.
How GPU-Bridge enables a new architecture
Three components work together to make your GPU supply chain resilient.
Cythos: GPUs in your VPC
Who is Self-Hosted for?
- If you are spending meaningfully on Azure, AWS, or GCP GPU instances and want to reduce cost — or need GPU compute outside the hyperscaler while keeping workloads and data entirely within your own VPC — this is for you.
What kind of workloads are supported?
Most GPU-heavy workloads work out of the box — LLM training, fine-tuning, inference, multi-modal AI, classic ML, statistical modeling, and scientific computation.
Is this secure?
- GPU access is over an encrypted SSL connection. Only outbound connections from your infrastructure — no inbound ports required.
- Your containers keep running inside your VPC. No data leaves your private network — only compute cycles move to the GPU.
How do you handle dependencies on internal private resources?
Your containers are still running inside your VPC — only the compute happens outside. Nothing needs to change in your ACL or networking rules. Your dependencies stay private and accessible as if the GPU is local to your infrastructure.
Is there a performance penalty?
- Launch time: a one-time increase of a few seconds to a minute, as the GPU attaches and initializes.
- Per-call latency: a few milliseconds per API call your application serves.
For long-running and batch applications, these are non-issues. Launch delay amortizes over the workload lifetime, and per-call overhead is negligible next to actual GPU compute time.
What happens to my data?
Your data is loaded directly into GPU VRAM and never stored outside your VPC. When your workload exits, GPU memory is wiped clean. Data residency requirements are naturally satisfied — nothing persists beyond the lifecycle of your job.
What about compliance?
We are SOC 2 Type II compliant. For additional requirements (HIPAA, PCI-DSS, FedRAMP, GDPR, or sector-specific frameworks), reach out at info@cloudexe.tech and we will walk you through how GPU-Bridge fits into your compliance posture.
How much work is a POC?
Very little. Copy our launcher binary inside your container and launch your workload command via it — the rest is automatic.
We've completed a full integration in as little as 15 minutes. Our team stays hands-on through your POC to make sure you hit value fast.
How mature is this product?
The GPU-Bridge technology is proven. The exact same tech stack underpins our DevCloud product, actively used by world-class universities for research workloads. You are getting battle-tested GPU virtualization technology applied to your private infrastructure.
DevCloud: Cloud GPU platform
Who is DevCloud for?
- AI developers, ML engineers, and researchers who want powerful GPU access without managing infrastructure. Best for startups, academic labs, and growing teams who need a ready-to-use platform rather than a self-hosted stack.
What kind of workloads are supported?
Most GPU-heavy workloads work out of the box — LLM training, fine-tuning, inference, multi-modal AI, classic ML, statistical modeling, and scientific computation.
Is this secure?
- GPU access is over an encrypted SSL connection. Connections are outbound — no ports need to be opened.
- GPU hardware is hosted at tier-1 neo-clouds with a state-of-the-art security posture.
Is there a performance penalty?
- Launch time: a one-time increase of a few seconds to a minute, as the GPU attaches and initializes.
- Per-call latency: a few milliseconds per API call your application serves.
For long-running and batch applications, these are non-issues. Launch delay amortizes over the workload lifetime, and per-call overhead is negligible next to actual GPU compute time.
How quickly can I get started?
You get a base instance ready in minutes. Running your first GPU workload requires no code changes — launch your existing command as-is.
Our team stays hands-on during onboarding to make sure you hit value fast.
How mature is DevCloud?
DevCloud is actively used by world-class universities for research workloads. The underlying GPU-Bridge technology has significant production miles behind it.
Pricing
Pay only for GPU time while your workloads run. No reservations, no idle charges, no setup fees. DevCloud only. Self-hosted pricing is negotiated separately.
What we're thinking about
Notes on inference infrastructure, confidential computing, and the economics of compute placement.
Trust Only When You Can't Verify
Trust, But Verify was good advice during the cold war. What does it mean today, when AI handles our most valuable data?
Decisions outlive their reasons
Inference infrastructure improves every few weeks, in unpredictable ways. Institutional workflows change over years.
Built by the people who built the GPUs
Who we are
We are a startup based in Silicon Valley. The founding team led iconic GPU programs at NVIDIA, Intel, and Google — shipping the first CUDA GPU, the first ARC GPU, and the core tech behind Google Stadia (cloud gaming platform).
Our investors
Engineering Capital
Get in touch
To schedule a demo, book time with us.
Or send us an email at info@cloudexe.tech.
Read our privacy policy.