Quantitative AI
WALLACE AI Supercomputer for Castle Ridge
Custom GPU system designed for a proprietary investment platform, sustained AI compute and future expansion.
Enterprise Compute Solutions
Servers and Multi-GPU Workstations
Trusted by Industry Leaders
Workstation Builds
Browse our previous Alpha PCs, built for Global Enterprise and Professionals.
Scientific Computing
Scientific Computing Workstation for Rutgers University
Configured for machine learning, fluid dynamics, mathematical modelling and sustained research workloads
Systems Integrator Deployment
12 Enterprise Workstations for an International Client Deployment
Alpha PC was the custom workstation supplier for a 12-system deployment using Threadripper PRO, NVIDIA RTX PRO graphics and 1TB ECC memory per system.
Systems Built Around the Workload
Model size, concurrency, data throughput, software and deployment needs determine whether your team needs an enterprise AI workstation, custom GPU server or on-prem AI infrastructure.
Locations We Serve
Supporting projects across Canada, the United States and eligible international markets.
North America
Available across Canada and the United States. We offer local pickup and delivery services throughout the GTA, including Toronto, Mississauga, Brampton, Markham, Vaughan, and surrounding areas.
Europe, Asia, & Australia
Fast and reliable shipping is available across Europe, Asia, and Australia. We ensure your custom PC arrives safely, no matter where you are.
Request an Enterprise Build
Fill out the form below to get your custom workstation quote. Our experts will review your requirements and get back to you within 24 hours.
What happens next
1
We review your workload + budget
2
You get a recommended spec + quote
3
We build, test, and deliver
A GPU is only one part of the system
A faster GPU will not solve a system limited by memory, storage, PCIe bandwidth, power or cooling. We design the full platform so your workload can use the compute you are paying for.
- Every workstation and server is validated under sustained load before delivery.
01
Accelerated computeGPU architecture and density matched to model size and throughput.
GPU
02
CPU & system memorySized for data preparation, orchestration and memory-heavy workloads.
Compute
03
NVMe & data storageFast storage for datasets, checkpoints, scratch space and long-term capacity.
Storage
04
Network & fabricPlanned around data movement, remote access and future scale.
Fabric
05
Power, cooling & operationsElectrical and thermal requirements reviewed before deployment.
Facility
Choose the Right Workstation or Server
Some workloads fit a local multi-GPU workstation. Others need centralized enterprise AI servers or infrastructure that can scale beyond one system. We help determine the right path before hardware is selected.
Enterprise AI Workstation
For specialists and smaller teams that need fast local access to AI, engineering, visualization or scientific workloads.
- 1 to 2 professional GPUs
- Office or lab deployment
- Direct local access
Multi-GPU Workstation
For larger models, local fine-tuning, scientific computing and sustained GPU workloads that need more memory or parallel compute.
- Higher GPU density
- ECC memory options
- Thermal and power planning
Custom GPU Server
For shared inference, centralized datasets, remote users and sustained compute in a rackmount environment.
- Enterprise GPU platforms
- Remote management and redundancy
- High-speed networking
Multi-Node AI Infrastructure
For organizations that need more aggregate GPU capacity, shared storage, high-speed fabric or room to scale beyond a single server.
- Multi-server planning
- Shared storage and networking
- Facility requirements
Enterprise Frequently Asked Questions
Find answers to common questions about our Workstations, Servers, and Services
An enterprise AI workstation is usually better when users need direct local access and the workload fits within workstation-class GPU, memory, power and cooling limits. A GPU server is better when compute needs to be shared, remotely accessed or deployed at higher density. Alpha PC reviews the workload before recommending either path.
We start with the models, GPU memory requirement, training or inference workload, expected concurrency, software stack, data throughput and growth requirements. The surrounding CPU, system memory, storage, networking, power and cooling are then sized around the GPU workload.
Yes. Alpha PC designs private and on-prem AI infrastructure for organizations that want dedicated hardware for local inference, RAG, model development and other internal AI workloads.
It depends on model size, precision, quantization, context length, batch size, concurrency and training method. Share the model and expected workload so the system can be sized for both current use and reasonable growth.
Yes. Scientific computing workstations can be configured around simulation, machine learning, modelling, imaging and other compute-intensive research workloads. CPU performance, GPU compute, memory capacity and storage throughput are selected around the application.
Yes. Workstations and servers can be configured around Linux or Windows environments and common AI tools such as CUDA, PyTorch, TensorFlow, Docker, Hugging Face and vLLM. Exact software and driver requirements should be provided during discovery.
Yes. Alpha PC supports business and institutional procurement, including RFQs, purchase orders, standardized configurations and multi-unit workstation deployments. Quote requirements and purchasing deadlines can be included with the configuration request.
Yes. Alpha PC supports projects in Canada, the United States and eligible international markets. International availability depends on the hardware, destination, warranty coverage, export requirements and delivery logistics.