OneSource Cloud
June 23, 2026
17 min read
Private LLM Deployment: Architecture Guide (2026)
Private LLM deployment requires dedicated GPUs, high-performance storage, and isolation. See the reference architecture enterprises use in 2026.
More Than Buying
GPU servers
Compute, networking, storage, power,
cooling, orchestration — under one roof
Overview
Modern AI infrastructure demands specialized architecture across every layer. OneSourceCloud delivers end-to-end GPU Cluster Design & Deploy services for enterprises, research labs, healthcare, universities, and AI startups — covering the full lifecycle from consulting to production.
Assessment & Planning
Workload profiling, GPU sizing, TCO analysis, deployment roadmap.
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Start with
the workload
AI Infrastructure Assessment & Planning
Phase 01
Our consulting team evaluates AI workload requirements, growth expectations, compliance constraints, and operational objectives — then translates them into an infrastructure plan you can budget, build, and grow with.
THREE Layers,
ONE Cluster.
Compute, network, and storage — designed as a single system
Phase 02
AI clusters require highly specialized architecture to maximize GPU utilization and distributed-training efficiency. Each layer is engineered for AI workload patterns and integrated end-to-end so nothing becomes the bottleneck.
Compute
GPU Compute Architecture
The right GPU platform, balanced with CPU, memory, PCIe lanes, NVLink, and orchestrator — for training, inference, or mixed workloads.
Services:
GPU server platform selection
CPU and memory balancing
PCIe lane optimization
NVLink & NVSwitch planning
GPU partitioning (MIG / vGPU)
Multi-node distributed training design
AI inference cluster optimization
Kubernetes & Slurm integration
STACK
Nvidia HGX
NVLink
MIG
Kubernetes
Slurm
Network
High-Speed AI Fabric
Distributed AI training requires ultra-low latency and lossless communication between GPU nodes. InfiniBand or RoCE, leaf-spine, RDMA end-to-end.
Services:
InfiniBand fabric design
RoCE network architecture
Spine-leaf, fat-tree, Clos planning
RDMA & GPUDirect integration
Congestion control tuning
Adaptive routing configuration
East-west AI traffic engineering
EVPN-VxLAN & OOB design
Technologies
InfiniBand
400G / 800G
GPUDirect
NCCL
UFM
Storage
AI Storage Architecture
Parallel file systems, RDMA data paths, and a storage tier sized to keep GPUs fed during training, checkpointing, and inference.
Services:
Parallel file system design
Hot & cold tiering strategy
Checkpoint throughput sizing
Dataset ingestion architecture
RDMA-enabled data paths
Storage fabric integration
Multi-tenant data isolation
Backup & disaster recovery
STACK
Lustre
GPFS
WekaFS
NVMe-oF
S3
Pick YourSilicon
Supported across NVIDIA, AMD, and hybrid environments
GPU Platforms
Whether it's frontier-model training on B200, production inference on L40S, or a mixed fleet that grew over time — we design, deploy, and operate against your hardware choice, not ours.
Nvidia
Flagship
B200
Architecture
Blackwell
Memory
192 GB HBM3e
NVLink
1.8 TB/s
Use case
Frontier training
Nvidia
H200
Architecture
Hopper
Memory
141 GB HBM3e
NVLink
900 GB/s
Use case
LLM training
Nvidia
H100
Architecture
Blackwell
Memory
80 GB HBM3
NVLink
900 GB/s
Use case
Training / inference
Nvidia
A100
Architecture
Ampere
Memory
40 / 80 GB
NVLink
7 instances
Use case
Workhorse AI
Nvidia
Inference
L40S
Architecture
Ada Lovelace
Memory
48 GB GDDR6
NVLink
350W
Use case
Inference / vis
Nvidia
RTX 6000 Ada
Architecture
Ada Lovelace
Memory
48 GB GDDR6
NVLink
300W
Use case
Workstation AI
Nvidia
AMD
Instinct MI300X
Architecture
CDNA 3
Memory
192 GB HBM3
NVLink
ROCm
Use case
LLM / HPC
Nvidia
mixed
Hybrid Fleet
Architecture
Heterogeneous
Memory
K8s / Slurm
NVLink
MIG / vGPU
Use case
Any mix
AI Density Breaks Traditional Facilities.
Power, cooling, and rack design for 60–120 kW racks
Phase 03
GPU clusters introduce power density and cooling requirements that traditional enterprise environments rarely handle. We engineer the facility envelope so the cluster runs at full rated performance — and scales.
Per-rack power, typical AI
60–120kW
Depending on GPU class and density.
Compare with ~5–10 kW for typical enterprise racks — a 10–20× jump in delivered power and dissipated heat.
Enterprise rack
5–10 kW
AI rack
60–120 kW
Facility Services
- Rack elevation planning & hot/cold aisle optimization
- High-density rack deployment
- Power distribution planning & redundant architecture
- UPS & generator capacity planning
- Liquid cooling readiness & integration
- Thermal airflow efficiency
- Cable management & structured cabling
- Physical security integration
- Remote & smart-hands planning
- Future expansion capacity preparation
From Staging to Production Day One
Hardware, software, and AI platform
— turnkey
Phase 04
A complete turnkey deployment: rack & stack, the full GPU software stack, and the AI platform users actually log into. You hand us the room — we hand you a running cluster.
Phase 04 · A
Hardware Deployment
Rack and stack services
GPU server installation
Network switch deployment
Storage system installation
GPU partitioning (MIG / vGPU)
Cabling and fiber deployment
Power validation
Hardware burn-in testing
Phase 04 · B
Software Deployment
Operating system installation
Kubernetes / Slurm deployment
NVIDIA GPU software stack
CUDA & NCCL configuration
AI framework installation
Driver & firmware management
Container runtime deployment
Multi-tenant configuration
Security hardening
Phase 04 · C
AI Platform Deployment
JupyterHub & notebook environments
Virtual cluster environments
GPU sharing & scheduling
Self-service provisioning portal
MLOps integrationAI workflow orchestration
User access management
ABAC policy configuration
Backup & disaster recovery
GPU Cluster
Frequently asked questions
Why do organizations need a dedicated GPU cluster instead of using public cloud AI services?
What GPU platforms does OneSource Cloud support?
Still have questions? Contact Us
How do you determine the right cluster size for an AI project?
Can OneSource Cloud deploy GPU clusters in existing data centers?
What networking technologies are recommended for distributed AI training?
Does OneSource Cloud provide ongoing management after deployment?
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