Run AI Inference on Dedicated GPUs — Without Leaving Canada
NVIDIA RTX & L40 GPUs · Dedicated Servers · Canadian Data Residency
Purpose-built for production inference workloads: LLM serving, OCR pipelines, computer vision, and document processing. Predictable monthly pricing, no noisy neighbours, no data leaving Canadian jurisdiction.
Why Canadian Businesses Choose Cloudspace for AI
Hyperscalers charge unpredictable GPU-hour rates and route your data through foreign jurisdictions. Cloudspace gives you dedicated hardware, flat-rate pricing, and guaranteed Canadian residency.
Canadian Data Sovereignty
Your models, your data, your prompts — all processed and stored in Canadian data centres. No CLOUD Act exposure. Designed to support PIPEDA, PHIPA, and provincial health privacy requirements.
Predictable Pricing
Flat monthly rates for dedicated GPU servers. No per-token surcharges, no surprise egress fees, no GPU-hour metering. Budget with confidence.
Dedicated, Not Shared
Your GPU is yours. No noisy neighbours competing for VRAM or compute cycles. Consistent inference latency for production workloads.
Low-Latency Inference
NVMe-backed storage, high-bandwidth networking, and GPU passthrough — built for sub-second response times on production inference APIs.
GPU Acceleration Options
Six NVIDIA GPUs across workstation and data-centre classes. Matched to your model size, throughput, and budget.
Workstation-Class GPUs
RTX 2000 Ada
16 GB GDDR6 · Ada Lovelace
Cost-effective GPU for lighter inference workloads. Ideal entry point for teams moving AI from development into production.
Best For
- OCR and document digitization
- Small-to-mid model inference (7B parameters)
- Image classification and object detection
- Embedding generation and search
16 GB
Ada Lovelace
Entry
GDDR6
RTX 4000 Ada
20 GB GDDR6 · Ada Lovelace
The workhorse for production inference. More VRAM and throughput for larger models, multi-stream pipelines, and concurrent workloads.
Best For
- LLM inference (13B–20B parameter models)
- Multi-page OCR and document pipelines
- Computer vision at scale
- Multi-tenant SaaS AI backends
20 GB
Ada Lovelace
Mid
GDDR6
NVIDIA L40
48 GB GDDR6 · Ada Lovelace
Premium inference GPU. 48 GB of VRAM handles large language models, high-throughput batch processing, and multi-model deployments on a single card.
Best For
- Large LLM inference (34B–70B+ quantized)
- High-throughput batch inference
- Multi-model serving (several models concurrently)
- RAG pipelines with large context windows
48 GB
Ada Lovelace
High
GDDR6 w/ ECC
Data-Centre Class GPUs
NVIDIA L40S
48 GB GDDR6 · Ada Lovelace
Purpose-built for inference at scale. Optimized tensor core performance and power efficiency make it the best price-to-performance ratio for production AI serving.
Best For
- High-throughput LLM inference at scale
- Cost-effective inference for 34B–70B models
- Video and image generation pipelines
- Multi-model serving with SLA requirements
48 GB
Ada Lovelace
91.6 TFLOPS
733 TFLOPS
NVIDIA A100
40 GB / 80 GB HBM2e · Ampere
The data-centre standard for mixed training and inference workloads. HBM2e memory delivers massive bandwidth for large-batch processing and model fine-tuning.
Best For
- Model fine-tuning and training runs
- Large-batch inference with high memory bandwidth
- Multi-instance GPU (MIG) for workload isolation
- Scientific computing and simulations
40 / 80 GB
Ampere
19.5 TFLOPS
312 TFLOPS
NVIDIA H100
80 GB HBM3 · Hopper
The latest-generation flagship GPU. Transformer Engine and FP8 acceleration deliver unmatched throughput for the most demanding LLM and generative AI workloads.
Best For
- Full-precision LLM training (70B+ parameters)
- Ultra-low-latency inference for generative AI
- Multi-node distributed training clusters
- Maximum throughput for mission-critical AI
80 GB
Hopper
67 TFLOPS
1,979 TFLOPS
Need multi-GPU configurations or custom builds? Talk to our infrastructure team.
Not Sure Which GPU You Need?
The right GPU depends on your model size, throughput requirements, and concurrency needs. Here's a quick guide:
What's your model size?
Models under 7B parameters fit on 16 GB (RTX 2000 Ada). 13B–20B models need 20 GB+ (RTX 4000 Ada). Quantized 70B models need 48 GB (L40 / L40S). Full-precision 70B+ training requires 80 GB HBM (A100 or H100).
Inference only, or training too?
For pure inference, the L40S offers the best price-to-performance. If you also need to fine-tune or train models, the A100 and H100 provide the HBM bandwidth that training workloads demand.
How many concurrent requests?
Higher concurrency means more VRAM consumed by KV-cache. For 10+ concurrent users, move up a tier. The A100 also supports Multi-Instance GPU (MIG) to partition a single GPU across isolated workloads.
Do you need to run multiple models?
If your pipeline chains several models (e.g., OCR → NER → classification), you'll want the VRAM to keep them all loaded. The L40S and A100 can serve multiple models concurrently without swapping.
Still not sure? Book a 15-minute call with our infrastructure team. We'll help you right-size your GPU based on your actual workload.
Built for Real Production Workloads
Not synthetic benchmarks. These are the workloads Canadian businesses are running on Cloudspace GPUs today.
OCR & Document Processing
Digitize faxes, scanned medical records, legal documents, and handwritten forms. GPU-accelerated OCR processes thousands of pages per hour with higher accuracy than CPU-only pipelines.
LLM Inference APIs
Self-host open-weight LLMs (Llama, Mistral, Phi) on your own infrastructure. Full control over model versions, context lengths, and data flow — no API calls leaving the country.
Document Intelligence Pipelines
Chain OCR, named-entity recognition, classification, and summarization into automated workflows. Extract structured data from unstructured documents at scale.
Multi-Tenant SaaS AI Backends
Power AI features across your SaaS product — search, recommendations, content generation — with isolated GPU resources per tenant or shared pools with workload scheduling.
Infrastructure That Scales With You
Start with a single GPU server. Scale to a private multi-node cluster when your workload demands it. No re-architecture required.
Single-Node to Multi-Node
Start with one GPU server for development and testing. Add nodes for horizontal scaling when you move to production or need higher throughput. Private networking between nodes keeps inter-node traffic off the public internet.
Container-Ready
Run Docker containers with GPU passthrough via NVIDIA Container Toolkit. Deploy Kubernetes clusters with GPU scheduling, or use simpler Docker Compose setups — your choice.
Customer Isolation
Dedicated GPU servers mean your workloads run on hardware that no other customer touches. Private VLANs, dedicated storage, and optional firewall rules ensure full tenant isolation.
Private Clusters
For regulated industries, we provision fully isolated clusters with dedicated networking, storage, and management planes. Meets the requirements of healthcare, legal, and financial compliance frameworks.
NVMe-Backed Storage
Fast model loading and dataset access on NVMe drives. No waiting for cold starts — models load from local NVMe into GPU VRAM in seconds, not minutes.
Flexible Networking
Public IPs, private VLANs, or both. Connect GPU nodes to your existing Cloudspace VPS or private cloud infrastructure over private networking with no egress charges.
Why Cloudspace Over a Hyperscaler?
AWS, Azure, and GCP are great for many things. Canadian AI inference with dedicated hardware, privacy guarantees, and predictable costs isn't one of them.
| Cloudspace | Typical Hyperscaler | |
|---|---|---|
| Data Residency | Guaranteed Canadian | Region-selectable, but subject to foreign law |
| GPU Allocation | Dedicated hardware | Shared (unless premium tier) |
| Pricing Model | Flat monthly rate | Per-hour + egress + API fees |
| Noisy Neighbours | None (dedicated) | Common on shared instances |
| Support | Direct access to engineers | Ticketing system, tiered support |
| Jurisdiction | Canadian-owned, no CLOUD Act | US-headquartered, subject to CLOUD Act |
Privacy-First AI Infrastructure
When your AI processes patient records, legal documents, or financial data, where that processing happens matters. Cloudspace GPU infrastructure is:
- Physically located in Canadian data centres
- Owned and operated by a Canadian company — no foreign parent entity
- Not subject to the US CLOUD Act or PATRIOT Act
- Aligned with PIPEDA and provincial health privacy acts (PHIPA, HIA). Designed with evolving Canadian privacy legislation in mind
Industries We Serve
Get Started in Days, Not Months
No 12-month commitments. No procurement bureaucracy. Talk to a human, get your GPU provisioned, start running inference.
Tell Us About Your Workload
Share your model size, throughput needs, and compliance requirements. We'll recommend the right GPU tier and server configuration.
We Provision Your Server
Dedicated GPU hardware, NVMe storage, private networking, and your choice of OS or container environment. Ready in days.
Deploy and Scale
SSH in, deploy your models, and go live. When you need more capacity, we add nodes to your cluster — same private network, same flat pricing.
Ongoing Support
Direct access to our infrastructure engineers. No ticket queues, no chatbots. If something breaks at 2 AM, you talk to a person who knows your setup.
Ready to Run AI Inference in Canada?
Tell us about your workload and we'll spec the right GPU configuration. No commitment, no sales pitch — just a technical conversation about what you need.
Most customers are running inference within a week of first contact.
Or call us directly: (888) 777-4705