Who this can fit
Frontier AI and enterprise-software teams
Typical work: LLM fine-tuning and inference
Planning focus: model-sized GPU memory and multi-GPU communication.
Enterprise compute for organizations in Silicon Valley and the San Francisco Bay Area
AI Workstations & GPU Servers for Silicon Valley Teams
Model size, throughput, power and utilization should decide whether capacity is owned, rented or split between both. Alpha PC helps Bay Area AI and robotics teams turn those numbers into a GPU workstation, shared AI server or hybrid plan without inventing benchmark results.
Planning a $50,000+ USD 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 LLM fine-tuning and EDA
For Silicon Valley and the San Francisco Bay Area, Alpha PC can quantify the discovery method around model size, tokens per second, scaling, power, utilization, and cloud economics without inventing benchmark results.
Who this can fit
Semiconductor-design organizations
Typical work: EDA and verification
Planning focus: high CPU throughput and memory bandwidth.
Who this can fit
Robotics, autonomy, biotech, and research groups
Typical work: Sensor fusion and 3D perception
Planning focus: sensor or dataset ingest and flexible accelerators.
Real Alpha PC work
Relevant Alpha PC work for frontier AI and enterprise-software teams
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 projectDocumented research workstation
Scientific Workstation for Rutgers University
See how machine learning, mathematics and fluid dynamics shaped a research workstation for Rutgers University.
Review the Rutgers projectPlan the right system
Bay Area AI capacity-fit calculator
Use these three options as a starting point, then validate them with a real workload.
How do model size, throughput, power, and utilization change the owned, cloud, or hybrid decision?
On tablets, scroll the table horizontally; on phones, each row becomes a decision card.
| System option | Best when | We configure | Confirm first |
|---|---|---|---|
| Workstation path: Model and GPU-memory fit | LLM fine-tuning and inference. | Model-sized GPU memory and multi-GPU communication. | Include exact versions for model, framework and serving stack. |
| Shared AI server: Throughput and concurrency | EDA and verification. | High CPU throughput and memory bandwidth. | Developer workstations support rapid iteration; lab servers and clusters require rack power, cooling, 100 or 400 GbE where justified, high-speed storage, and scheduling. |
| Staged deployment: Power and sustained use | Sensor fusion and 3D perception. | Sensor or dataset ingest and flexible accelerators. | Bay Area projects should capture USD budget, California destination, tax handling, rapid-growth or enterprise purchasing, approved substitutions, allocation risk, receiving, and facility readiness. |
Owned capacity or cloud: Model a three-year mix of owned and cloud GPUs using actual utilization, reservations, egress, storage, queue delay, staffing, power, and capacity risk.
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 LLM fine-tuning and inference?
Use representative models, context lengths, concurrency, checkpoints, design databases, build trees, sensor logs, and scientific data to measure throughput, time, VRAM, scaling, power, and thermals.
When does EDA justify shared compute instead of another workstation?
Developer workstations support rapid iteration; lab servers and clusters require rack power, cooling, 100 or 400 GbE where justified, high-speed storage, scheduling, remote management, and staged node growth. Model a three-year mix of owned and cloud GPUs using actual utilization, reservations, egress, storage, queue delay, staffing, power, and capacity risk.