Who this can fit
AI laboratories and software companies
Typical work: Model training and fine-tuning
Planning focus: GPU memory and multi-GPU communication.
Enterprise compute for organizations in Greater Montreal
AI, VFX & Aerospace Workstations for Greater Montreal
AI research, aerospace simulation and VFX each pull a system in a different direction. Alpha PC helps Greater Montreal organizations choose a quiet workstation, a multi-GPU system or a shared AI server based on software, data movement, utilization and the people who need access.
Planning a $50,000+ CAD project? Start with the workload. A finished parts list can come later.
- Workload reviewed firstSystems Engineering checks the software, data flow, site limits, and acceptance needs.
- Real project evidenceSee how Alpha PC handled sustained AI compute, custom cooling and future expansion for the WALLACE platform.
- Quote assumptions in writingCurrency, delivery, substitutions, support, and warranty are stated in the quote.
$50,000+ projects
Request a quote
Tell us what the system must run and the budget range. Add only the technical details you already know.
- A recommendation tied to the workload
- A configuration your technical team can review
- Delivery assumptions written into the quote
Start with six required fields. Technical details are optional.
Where Alpha PC can help
Workstations and shared systems for model training and CAE
In Greater Montreal, Alpha PC uses separate architecture logic for AI research, aerospace engineering, and media production, not one broad technology pitch.
Who this can fit
Aerospace, VFX, animation, and game teams
Typical work: CAE, flight, and structural simulation
Planning focus: CPU and GPU balance and professional drivers.
Who this can fit
Life sciences, quantum, cyber, and university research
Typical work: Computational biology and imaging
Planning focus: large ECC RAM and reproducible environments.
Real Alpha PC work
Relevant Alpha PC work for AI laboratories and software companies
Real Alpha PC work and practical guidance for this decision.
Documented AI infrastructure
WALLACE AI Supercomputer for Castle Ridge
See how Alpha PC handled sustained AI compute, custom cooling and future expansion for the WALLACE platform.
Review the WALLACE projectProfessional workstation guide
High-Performance Computers for Digital-First Professionals
Review the workstation considerations for professional applications, large project files and sustained daily use.
Read the workstation guidePlan the right system
Greater Montreal three-track architecture map
Use these three options as a starting point, then validate them with a real workload.
Is the buying decision driven by AI research, aerospace engineering, or media production?
On tablets, scroll the table horizontally; on phones, each row becomes a decision card.
| System option | Best when | We configure | Confirm first |
|---|---|---|---|
| Workstation path: AI research and model work | Model training and fine-tuning. | GPU memory and multi-GPU communication. | Include exact versions for framework, CUDA and renderer. |
| Shared AI server: Aerospace simulation | CAE, flight, and structural simulation. | CPU and GPU balance and professional drivers. | Studios may favour quiet artist workstations and centralized storage, while research and AI teams may need rack GPUs, 25 or 100 GbE. |
| Staged deployment: VFX and virtual production | Computational biology and imaging. | Large ECC RAM and reproducible environments. | Greater Montreal projects should identify CAD budget, GST and applicable Quebec tax handling, English documentation needs, procurement language, receiving, and approved substitutes. |
Owned capacity or cloud: Rendering bursts and research experiments can suit cloud capacity, while steady inference, protected data, and daily artist or engineer use may favour owned systems.
Not sure which option fits yet?
Share the workload. We will help define the system.
Request a quote
From workload to delivery
From workload notes to delivery
Three steps take one real workload to a configuration, quote, and delivery plan your team can check.
- 1
Describe one real workload
Share the work, software, data, users, and the constraint that is slowing the team down.
- 2
Review the design
Alpha PC ties those requirements to a configuration, quote assumptions, and the points still to be confirmed.
- 3
Validate and deliver
Testing, acceptance criteria, and delivery responsibilities are set before the system ships.
Explore more Alpha PC resources
Explore the systemsAI workstationsEnterprise AI servers
Common questions
Questions before the quote
Short answers to the questions that can change the build.
Which models, data, and response targets should drive model training and fine-tuning?
Use representative models, scenes, meshes, image sets, and scientific datasets to report memory use, throughput, render or solve time, storage demand, power, and sustained thermals.
When should Montréal aerospace CAE move to shared compute?
Studios may favour quiet artist workstations and centralized storage, while research and AI teams may need rack GPUs, 25 or 100 GbE, scheduler access, redundant data paths, and remote administration. Rendering bursts and research experiments can suit cloud capacity, while steady inference, protected data, and daily artist or engineer use may favour owned systems.