Rta LabsInference infrastructure in Canada, matched to your traffic.
Rta Labs Compute sources production inference infrastructure matched against throughput, latency, memory, geography and cost, starting with Canadian options. Describe the model and the traffic; Rta Labs checks which provider options fit.
What Rta Labs sources
- Dedicated or shared GPU capacity for serving models in production
- API endpoints, containers or bare metal, depending on how you deploy
- Capacity sized to average and peak traffic, with room to grow
- Options near your users, with Canadian residency where required
What to include in your request
- Model family and size or parameter range; serving framework if fixed
- GPU preference and minimum VRAM
- Traffic: average and peak requests per second, tokens per second, concurrency
- Latency target and uptime target
- Where your users are, and any Canadian residency requirement
- Autoscaling, dedicated or shared, and API, container or bare metal
- Expected monthly spend and growth over six months
For each major point, say whether it is required, preferred or flexible.
Canadian location, residency and control
Rta Labs Compute is Canada-first. You can require Canadian location, set a preferred province or metro, and say whether a region is required or only preferred.
You can require Canadian data residency or a Canadian-controlled provider. Rta Labs verifies these per provider and per requirement.
Physical location alone does not make a provider sovereign. Rta Labs does not label a provider sovereign on location alone.
When a workload allows it, Rta Labs also checks other North American options.
Inference Infrastructure in Canada: common questions
Do I need to share my model or prompts?
No. Never send model weights or proprietary prompts. Describe the model family, size and traffic; Rta Labs only needs infrastructure requirements.
Can inference run in Canada for Canadian users?
You can require Canadian location and data residency, or state where your users are. Rta Labs verifies residency and provider control per provider rather than assuming it from location.
How does Rta Labs compare inference options?
Options are normalized on GPU, memory, location, deployment model, term, service levels and total cost, and each option shows which of your required and preferred requirements it meets.
Capacity, pricing and deployment dates are confirmed with providers before a formal option is presented. Provider identities stay private until an introduction is approved.
Tell us what you need.
A person at Rta Labs reads every request.
Also sourced: GPU compute in Canada · AI colocation in Canada