Talk to us to size a GPU fleet, map your network topology, and stand up your virtual AI infrastructure.
Contact: contact@canvasfoundry.com
AI today is largely driven by LLMs and multimodal models that learn from huge datasets and then serve fast responses at scale. That requires GPU acceleration, high-bandwidth networking, and reliable power/cooling, not just “a server.”
Servers + GPUs (A100/H100/B100 class acceleration, multi-tenant or dedicated)
Networking (high-throughput fabric, controllable L2/L3 topology)
Power + cooling (designed for sustained, dense GPU workloads)
A virtual datacenter you control (not a black-box instance rental)
Force you into a specific “neocloud” platform
Replace your tools, workflows, or operating model
Require you to re-architect everything around someone else’s Kubernetes
Most GPU clouds make you adapt: their Kubernetes, their SSH workflow, their networking assumptions, their guardrails.
CanvasFoundry does the opposite: we bring the GPU datacenter to your existing environment: your VMs, your servers, your data, your tooling.
•Keep your current stack (VMs, pipelines, CI/CD, on-prem integrations)
•Connect to your data where it already lives
•Expand capacity without migrating your whole operating model
CanvasFoundry virtualizes more than compute. You create the network itself.
Every connection you design is a real, enforceable topology, under your control.
•Build isolated tenant networks (including overlapping IP space)
•Insert your own routers/firewalls/load balancers exactly where you want
•Verify every hop and prove separation, instead of “trust us”
This is the difference between renting GPUs and running your own AI datacenter experience.