Training & fine-tuning
Dense GPU compute, high-bandwidth memory and storage throughput planned around datasets, checkpoints and training windows.
Enterprise AI infrastructure · projects from $50,000
Enterprise AI Servers, GPU Workstations & Private AI Infrastructure
Alpha PC designs workload-matched systems for AI training, inference, private LLMs, RAG, computer vision, simulation and high-performance research. Canada, the United States and screened international destinations.
- Rackmount and workstation platforms
- GPU, storage, network and power planning
- Integration, validation and deployment options
Workload fit
Infrastructure shaped by what your team needs to run
Start with model size, concurrency, data movement, software and deployment constraints. Then choose the platform.
Private LLM, RAG & inference
On-premises model serving, retrieval pipelines and multi-user inference for teams that need local control and predictable capacity.
Simulation & HPC
CPU/GPU balance, memory bandwidth and fast interconnects for scientific modelling, engineering, analytics and parallel workloads.
Multi-user AI development
Shared development infrastructure for researchers, data scientists and engineering teams working across containers and environments.
Rendering & digital twins
Multi-GPU systems for visualization, virtual production, architecture, digital twins and GPU-accelerated content pipelines.
Quantitative & data analytics
High-memory compute and fast local storage for financial research, large datasets and continuously evolving analytical workflows.
Computer vision & imaging
GPU workstations and inference servers sized for training, image analysis, video streams, quality inspection and medical or scientific imaging.
NLP, speech & recommendation
Accelerated systems for document processing, transcription, embeddings, ranking, recommendation engines and multimodal application development.
Whole-system architecture
A GPU is only one part of a production-ready system
Alpha PC plans the surrounding platform so expensive accelerators are fed, cooled, connected and serviceable.
- Size GPU memory and quantity against model, precision and concurrency targets.
- Map PCIe topology, CPU lanes, memory capacity and storage throughput together.
- Confirm rack depth, input power, PDU, heat rejection, networking and service access before deployment.
01
Accelerated computePCIe, NVL or HGX paths sized to the workload
GPU
02
CPU & system memoryData preparation, orchestration and memory-resident work
Compute
03
NVMe & data storageDatasets, checkpoints, scratch and resilient capacity
Storage
04
Network & fabricClient access, data movement and scale-out planning
Fabric
05
Power, cooling & operationsElectrical, thermal, management and support requirements
Facility
Configuration paths
Plan the complete deployment, not an isolated box
Start with one high-density system or scope the surrounding storage, fabric and deployment services at the same time.
Rackmount GPU servers
2U to high-density platforms for PCIe, NVL, HGX and scale-out AI deployments.
Multi-GPU workstations
Desk-side or rackmount systems for local AI development, research, visualization and private inference.
Storage & networking
NVMe tiers, resilient capacity, high-speed NICs and switching planned around data movement.
Power & cooling
Input voltage, circuit, PDU, rack, airflow and heat-load checks before the configuration is finalized.
Integration & deployment
Assembly, firmware alignment, burn-in, documentation, delivery and installation options.
Purpose-built
Local AI compute without a generic configuration
Custom AI workstations
AI workstations for local models, deep learning and professional GPU compute
Choose a quiet desk-side system, a high-capacity multi-GPU workstation or a rackmount workstation node. Alpha PC can scope the platform for machine learning, generative AI, private LLM inference, RAG, computer vision, rendering, simulation or quantitative research.
1–7GPU platform paths
Up to 4 TBECC system memory paths
Up to 192CPU-core platform paths
Maximums are platform-dependent and may require rackmount or server-class architecture. The final bill of materials is validated for GPU spacing, PCIe lanes, power, cooling, memory and software compatibility.
- Windows, Ubuntu or Linux deployment paths based on the application stack.
- Multi-GPU power delivery, airflow, driver consistency and sustained-load validation.
- NVMe scratch, resilient storage and 10/25/100GbE options where the workflow requires them.
PyTorch
TensorFlow
CUDA
Docker
Kubernetes
Hugging Face
vLLM
Stable Diffusion
System class
Choose the deployment class after the workload is measured
The right answer may be a single professional workstation, a dense GPU server or a multi-node AI cluster. These paths help procurement teams frame the decision.
Professional AI workstation
Best for one or a few specialists who need low-latency local access for AI development, visualization, CAD or inference.
- 1–2 professional GPUs
- Office or lab deployment
- Direct interactive use
Multi-GPU workstation
Best for larger models, local fine-tuning, research teams and sustained CUDA workloads that still fit a workstation-class environment.
- Platform-dependent GPU density
- ECC memory and high-core CPUs
- Thermal and acoustic planning
Rackmount GPU server
Best for shared inference, centralized datasets, remote access, OEM support paths and data-centre power and cooling.
- PCIe, NVL or HGX architectures
- Remote management and redundancy
- High-speed network options
Multi-node AI infrastructure
Best when training windows, aggregate throughput, many users or growth targets require fabric, storage and rack-scale design.
- Cluster and fabric planning
- Shared data architecture
- Facility and deployment scope
Workload sizing guide
What determines the right AI server or workstation?
Model size alone is not enough. Precision, context, batch size, concurrency, data movement, software support and deployment constraints can change the recommended system.
| Workload | Typical system path | Primary sizing inputs | Infrastructure questions |
|---|---|---|---|
| Local LLM inference & RAG | AI workstation or shared inference server | Model parameters, quantization, context length, concurrent users and latency target | Vector database, document volume, privacy boundary and uptime |
| Training & fine-tuning | Multi-GPU workstation, GPU server or HGX platform | Model, precision, batch size, optimizer state, dataset and training window | Checkpoint throughput, scaling efficiency, interconnect and expansion |
| Computer vision & video AI | Workstation or PCIe inference server | Resolution, stream count, frames per second, model and preprocessing | Ingest bandwidth, capture devices, retention and edge versus central use |
| Generative AI, rendering & digital twins | Professional GPU workstation or render server | Scene complexity, assets, resolution, render engine and interactive target | Certified drivers, display needs, collaboration and render scheduling |
| Scientific HPC & simulation | CPU/GPU node or scale-out cluster | Solver, code scaling, CPU/GPU balance, memory footprint and precision | Fabric, storage, scheduler, licensing and facility limits |
| Bulk AI workstation procurement | Standardized workstation fleet | User personas, approved software, lifecycle, quantity and refresh plan | Golden image, asset tagging, warranty, staging and phased delivery |
This guide frames discovery; it is not a compatibility promise. Alpha PC validates the selected hardware against the final software stack, workload and deployment environment.
Platform choice
Enterprise server families matched to your requirements
Compare current enterprise GPU server families from leading OEMs. Exact models, accelerators, firmware, support geography and availability are confirmed against the final quote.
PowerEdge AI & accelerated servers
PowerEdge XE9780/XE9785, XE9680L, R7725 and related configuration paths for B200/B300, HGX and PCIe accelerators.
Dense training, inference and enterprise data-centre operationsProLiant & Cray AI platforms
ProLiant Compute DL380a Gen12, DL384 Gen12 and related GPU-capable ProLiant or Cray system paths.
High-density PCIe inference, fine-tuning and accelerated computeThinkSystem accelerated compute
ThinkSystem SR675 V3 and related GPU server families for PCIe or dense accelerated workloads.
AI, HPC, visualization and scale-out clustersGPU SuperServer systems
SYS-822GS-NBRT-class HGX B200 systems plus PCIe and high-density 4U/8U GPU server designs.
Flexible integration and high accelerator densityG-series GPU servers
G893 HGX B200/H200 and G494-series configuration paths for training, inference and HPC.
HGX and multi-GPU PCIe compute for data-intensive workloadsConfiguration, regional availability, warranty, support coverage and lead time are verified for the exact bill of materials before an order is accepted.
Accelerator paths
Select memory, density and interconnect for the workload
Final GPU support depends on the chosen server, power envelope, cooling design, firmware and manufacturer validation.
NVIDIA H100 & H200
PCIe, NVL and HGX paths for training, inference, analytics and high-memory AI workloads; H200 options can provide 141 GB of HBM3e per GPU.
B200, B300 & GB300 paths
Current high-density Blackwell platform paths for advanced training and large-scale inference, subject to OEM qualification, allocation and compliance review.
RTX PRO 6000 Blackwell
96 GB ECC workstation and server editions for local AI, visualization, simulation, rendering and professional multi-GPU workloads.
Workload-matched alternatives
Recommend the accelerator only after reviewing software compatibility, memory demand, precision and deployment constraints.
Private AI & on-premises RAG
Keep models and sensitive data inside the environment you control
A private AI server can support local LLM inference, retrieval-augmented generation, embeddings, fine-tuning and internal copilots without making public cloud capacity the default. Discovery still needs to cover identity, network segmentation, data governance, backup and operational ownership.
Ingest & retrieval
Document volume, OCR, chunking, embeddings, vector storage and refresh cadence.
Model serving
Model family, precision, context length, tokens per second, concurrency and high availability.
Security boundary
Users, authentication, segmentation, encryption, audit needs and data-residency requirements.
Operations
Containers, orchestration, monitoring, backups, updates and the team responsible for production.
Global enterprise projects
AI server procurement for Canada, the United States and screened international destinations
Canada and the United States appear first in the request form. Eligible inquiry destinations follow alphabetically, with project-level review before any hardware is quoted or released.
Destination & entity review
Screen the delivery location, purchasing organization, ultimate parent, end user and all relevant restricted-party information.
Product & end-use review
Confirm accelerator classification, intended workload, diversion risk, sanctions, export-control requirements and any licence conditions.
Commercial & delivery plan
Validate OEM allocation, warranty geography, freight, insurance, import responsibilities, power, rack access and installation scope.
Country selection means the inquiry can be reviewed; it is not automatic shipment approval.
Final eligibility depends on the exact GPU and system, destination, end user, ultimate parent, end use, restricted-party screening, licensing, OEM policy and applicable Canadian, U.S. and local requirements at the time of the transaction.
Procurement process
From requirements to a procurement-ready configuration
The quote is built around technical fit, deployment realities and the support route—not a generic parts list.
Discovery
Share workloads, models, software, users, data size, GPU count, destination, organization ownership, timeline and budget.
Architecture & quote
Review the platform, accelerator topology, memory, storage, network, power, support, compliance route and commercial terms.
Integration & validation
Complete assembly, firmware alignment, burn-in testing and documentation according to the agreed scope.
Delivery & handoff
Coordinate screened fulfillment, insured delivery, site requirements, installation options and the final warranty/support route.
Project evidence
Systems built around real research and enterprise workloads
See how Alpha PC translated specialized requirements into complete, workload-matched systems.
Quantitative AI
WALLACE AI supercomputer for Castle Ridge
A purpose-built system designed around a proprietary investment platform, sustained compute, cooling, expansion and long-term use.
Read the project
University research
AI research workstation for Rutgers University
Configured for machine learning, fluid dynamics, mathematical modelling and sustained research sessions in a lab environment.
Read the projectEnterprise FAQ
Questions to resolve before procurement
Use the request form to provide project-specific constraints that affect the final answer.
How long does an enterprise AI server project take?
Lead time depends on the exact server, accelerator allocation, custom integration, testing scope and destination. The written quote confirms the current estimate before the order is accepted.
How are power and cooling requirements confirmed?
Before final configuration, confirm available input voltage, circuit capacity, PDU and connector type, rack depth, airflow direction, ambient conditions and heat-rejection capacity. High-density systems can require facility planning beyond a standard office circuit.
What warranty and support are included?
Warranty owner, support level, service geography and any Alpha PC integration support are documented for the final bill of materials. Coverage varies by OEM, component, country and selected service plan.
Can Alpha PC review international AI server projects?
Yes. Canada and the United States are listed first, followed by the other destinations currently accepted for inquiry. Every international project remains subject to export-control, sanctions, restricted-party, ultimate-parent, end-user, end-use, licensing, OEM and logistics review. A country appearing in the form is not automatic shipment approval.
Can the system support private or on-premises AI?
Yes. A configuration can be designed for local training, inference, RAG and sensitive-data workflows. Security, network segmentation, software, identity and data-governance requirements should be included in discovery. Do not place confidential model or dataset contents in this public web form.
Do you provide installation and deployment support?
Available options can include integration, burn-in, documentation, insured delivery, remote handoff and project-specific on-site support. The final scope depends on the system and destination.
Can the quote be prepared in CAD or USD?
Select CAD or USD in the request form. The formal quote states its currency, taxes, shipping, payment terms, validity period and any exchange-rate conditions.
How much GPU memory does an AI workstation or LLM server need?
GPU memory depends on the model, parameter count, precision or quantization, context length, batch size, concurrency, training method and headroom. Share the actual model and target workload so the recommendation accounts for both present use and practical growth.
Can the system be configured for Ubuntu, Linux, CUDA and containers?
Yes. Ubuntu and Linux are common for CUDA, PyTorch, TensorFlow, Docker, Kubernetes, Hugging Face and vLLM workflows. Windows or dual-environment paths can also be reviewed. Exact driver, framework and operating-system versions should be included in discovery.
How do you improve multi-GPU workstation stability?
The platform must be designed around PCIe lanes and topology, GPU spacing, power delivery, airflow, thermals, memory, firmware and driver consistency. The agreed validation scope can include sustained-load testing and documented handoff rather than only a short power-on check.
Do you plan NVMe storage and high-speed networking?
Yes. The design can include boot, scratch, dataset, checkpoint and resilient-capacity tiers plus 10, 25, 100GbE or workload-appropriate fabric. Required throughput, capacity, redundancy and connection to existing NAS, SAN or cluster storage should be specified.
Can multiple AI workstations be standardized for a team or lab?
Yes. Bulk AI workstation procurement can be scoped around user personas, an approved configuration, operating-system image, asset tagging, documentation, warranty, staging and phased delivery. Alternate-component rules should be agreed before purchasing to protect fleet consistency.
Request a configuration
Tell us what the system needs to accomplish
This form is for enterprise projects starting at $50,000. Share enough context to begin technical, procurement and destination screening.