What infrastructure is required for private AI
Quick Answer
Companies implementing private AI need a balanced infrastructure stack that covers compute (CPU/GPU), memory, storage, networking, and orchestration, sized to their use cases and growth horizon. For mid‑market B2B organizations, the goal is not to mimic hyperscalers, but to design an architecture that delivers acceptable latency and reliability for LLM inference and retrieval while remaining cost‑efficient and scalable.
💡 AgenixHub Insight: Based on our experience with 50+ implementations, we’ve found that careful workload scoping and quantization of models can reduce compute requirements by 30–50% versus naive sizing. Get a custom assessment →
What “private AI infrastructure” means
Private AI infrastructure is the combination of hardware, networking, and platform software used to run AI models inside your own controlled environment (on‑prem, private cloud, or isolated VPC), instead of relying on public multi‑tenant APIs.
In 2025, hardware is projected to represent roughly 39–40% of enterprise LLM market demand, reflecting the need for GPUs, accelerators, and high‑performance storage to run models efficiently. At the same time, cloud and hybrid deployment dominate overall LLM adoption, which means many mid‑market companies blend owned hardware with reserved or on‑demand GPU capacity.
AgenixHub typically designs private AI for mid‑market firms as a layered architecture:
- Compute: CPU and GPU tiers sized for inference and light fine‑tuning.
- Storage: High‑throughput NVMe for indexes and logs; object/file storage for corpora and checkpoints.
- Network: Low‑latency, high‑bandwidth links between AI nodes and data sources.
- Orchestration: Kubernetes or equivalent for scaling, plus observability and security layers.
Compute: CPU vs GPU for private AI
CPU and GPU roles
Well‑designed private AI infrastructure uses CPUs and GPUs for different tasks:
- CPUs:
- Orchestration, API gateways, ETL and retrieval logic, vector search, and smaller models.
- In some scenarios, optimized CPU inference for smaller models (sub‑1B parameters) can match or outperform GPUs due to lower overhead and better thread utilization.
- GPUs:
- Latency‑sensitive LLM inference (e.g., 7B–70B parameter models).
- Fine‑tuning and heavy embedding generation.
- High‑throughput batch processing of prompts.
Benchmarks and analyses show that GPU servers are significantly more power‑hungry than traditional servers (multiples of 5x–6x power consumption), but deliver far higher throughput for large models. This makes right‑sizing and utilization critical for mid‑market budgets.
Sizing compute for typical mid‑market use cases
For a mid‑market B2B company (50M–500M revenue) running internal copilots, knowledge search, and support assistants, AgenixHub commonly sees:
- Entry‑level stack (pilot / single department):
- 1–2 GPU servers with 1–2 mid‑range GPUs each (e.g., 40–80 GB per GPU), plus 2–4 CPU‑only nodes.
- 256–512 GB RAM across CPU nodes.
- Suitable for:
- 50–200 concurrent internal users.
- One 7B–13B parameter model with quantization and RAG.
- Growth stack (multiple business functions):
- 2–4 GPU servers, 4–8 GPUs total.
- 512 GB–1.5 TB RAM in aggregate.
- Mix of GPU types (high‑memory GPUs for main LLM, more cost‑effective GPUs for embedding jobs).
- Supports:
- 200–1,000 concurrent users.
- Multiple models (specialized summarization, code assists, domain‑specific LLMs).
- Advanced stack (regional or global deployment):
- 4–8 GPU servers, 8–32 GPUs total, often in a hybrid configuration with cloud burst.
- 1–3 TB RAM across compute cluster.
- Designed for:
-
1,000 concurrent users and several distinct AI applications.
- Light fine‑tuning or LoRA adaptation on proprietary data.
-
AgenixHub typically helps clients choose between CPU‑only, GPU‑light, and GPU‑heavy designs based on target latency (e.g., sub‑1s responses for chat vs batch analytics), concurrency, and budget.
Model training vs inference: infrastructure differences
Training or heavy fine‑tuning
Full training of frontier‑scale LLMs is out of scope for most mid‑market firms given the capital required for multi‑thousand‑GPU clusters and specialized interconnects.
Realistic mid‑market training scenarios:
- Domain adaptation, LoRA, or parameter‑efficient fine‑tuning on top of open‑weight models.
- Small custom models (e.g., 0.5B–3B parameters) for domain‑specific tasks.
Infrastructure implications:
- 4–16 GPUs with high memory (40–80 GB), NVLink or equivalent for fast GPU‑GPU communication, and high‑bandwidth storage.
- Strong cooling and power provisioning, as GPU servers consume significantly more energy than standard servers.
AgenixHub generally advises mid‑market clients to:
- Use owned or dedicated GPUs for inference and light fine‑tuning.
- Leverage specialized providers for heavy training if ever needed, to avoid under‑utilized capex.
Inference and retrieval‑augmented generation (RAG)
Inference and RAG are the dominant workloads for mid‑market private AI:
- GPU needs:
- Real‑time chat and summarization workloads.
- RAG pipelines combining retrieval, re‑ranking, and generation.
- CPU opportunities:
- Smaller distilled models for specific tasks.
- Batch workloads or background processing where latency is less critical.
AgenixHub often deploys:
- GPU‑accelerated inference pods for main AI assistants.
- CPU‑only or CPU‑primary pods for cheaper, lower‑priority tasks (e.g., nightly document embedding, offline indexing), improving overall ROI.
Memory and storage requirements
RAM and GPU memory
Larger models and context windows drive high memory requirements:
- GPU memory:
- For 7B–13B parameter models at common quantizations, 20–40 GB GPU memory is typically sufficient for inference.
- Larger models (30B–70B) benefit from 80 GB or more, or sharding across multiple GPUs.
- System RAM:
- RAG stacks and vector databases are often RAM‑intensive; 128–256 GB per node is common in production‑grade setups.
Emerging architectures and technologies (e.g., CXL and HBM) provide higher memory bandwidth and pooling options, enabling servers to allocate memory dynamically across CPUs and GPUs for demanding AI workloads.
Storage tiers for private AI
Private AI infrastructure usually has three main storage tiers:
- Hot storage (NVMe SSD):
- Vector databases, search indexes, and active model checkpoints.
- Requires high IOPS and low latency.
- Warm storage:
- Corpora of documents used for RAG but not accessed every second (e.g., file shares, document repositories).
- Often enterprise NAS or object storage with caching.
- Cold/archival storage:
- Historical logs, old training data versions, backups.
AgenixHub often sees mid‑market deployments allocate:
- 10–20 TB NVMe for hot data and checkpoints in initial deployments.
- 100–300 TB of object or file storage for corpora, logs, and backups, depending on how much historical data is brought into the AI stack.
Networking and interconnects
Internal network for AI workloads
AI workloads require higher bandwidth and lower latency than most legacy enterprise apps.
Key considerations:
- East‑west traffic:
- Between GPU servers, vector databases, and orchestration nodes.
- For distributed inference or training, 100 Gbps or higher per node is common; leading AI clusters use 200 Gbps+ or specialized fabrics.
- North‑south traffic:
- Between user devices/internal services and AI gateways.
- Standard 10–25 Gbps uplinks are often sufficient for mid‑market volumes, as user queries are small compared to internal data movement.
AgenixHub typically designs AI clusters on top of:
- Redundant 25–100 Gbps links for mid‑market clients, with QoS tuned so AI jobs do not starve other critical systems.
- Segmented networks and strict firewall rules, as private AI environments often process high‑value data.
WAN, cloud, and hybrid considerations
Because cloud remains the leading deployment mode for enterprise LLMs, many mid‑market companies adopt hybrid patterns: local gateways and vector stores with cloud GPUs for surge capacity.
Infrastructure implications:
- Dedicated, high‑reliability links (e.g., private interconnects) between on‑prem data centers and cloud regions to minimize latency and jitter.
- Careful routing and data residency controls for regulated data.
AgenixHub’s designs often prioritize:
- Keeping sensitive corpora and indexes in‑region or on‑prem.
- Using cloud GPUs for burst inference/fine‑tuning on anonymized or minimized datasets.
Orchestration, scaling, and reliability
Containerization and cluster management
Modern AI infrastructure typically runs on containers and orchestration platforms:
- Kubernetes or equivalent platforms manage:
- Autoscaling of AI services based on load.
- Placement of GPU workloads.
- Health checks, rollouts, and failover.
Guides and practitioners emphasize that AI workloads must be orchestrated as part of a unified platform with monitoring, logging, and security, not run as ad‑hoc scripts on “pet” servers.
AgenixHub often deploys:
- A separate AI‑focused Kubernetes cluster (or dedicated node pools) with GPU nodes labeled accordingly.
- Ingress controllers and service meshes to secure and monitor traffic between AI components.
Scalability strategies
Scalability for private AI is about both vertical and horizontal growth:
- Vertical scaling:
- Adding more powerful GPUs or more memory to existing nodes.
- Horizontal scaling:
- Adding more GPU nodes and sharding workloads or using model parallelism.
For mid‑market firms, the most practical pattern is:
- Start with modest GPU capacity sized for pilot workloads.
- Design the network, storage, and orchestration layer so adding more GPU nodes does not require a full redesign.
AgenixHub emphasizes capacity planning tied to business metrics (number of active users, expected queries per day, acceptable latency) and uses those to size GPU/CPU requirements quarterly.
Power, cooling, and physical considerations
GPU‑heavy servers drive substantial power and cooling requirements:
- Analyses of next‑gen AI and HPC infrastructure show GPU servers can consume nearly 6x the power of traditional servers, requiring careful planning for racks, PDUs, and cooling systems.
- High‑density GPU racks may require:
- 30–60 kW per rack.
- Hot/cold aisle containment or liquid cooling for sustained loads.
AgenixHub often works with facilities teams early to:
- Assess whether existing data centers can support the planned GPU density.
- Consider colocation or edge‑data‑center solutions if on‑prem power/cooling is limited.
Cost and scaling dynamics for mid‑market
Capex vs opex balance
In practice:
- Owned GPU infrastructure:
- Higher up‑front capex but lower marginal cost per token for steady workloads.
- Cloud‑based GPUs:
- Lower up‑front investment but higher variable costs; good for spiky or experimental workloads.
Enterprise LLM reports show strong growth in both hardware and cloud components, with cloud deployment representing over half of LLM market value due to scalability and lower initial cost.
AgenixHub typically recommends:
- A hybrid strategy for mid‑market B2B:
- Anchor capacity on a right‑sized on‑prem or dedicated GPU cluster (e.g., 2–4 GPU servers).
- Use cloud for experimentation and burst demand.
- Quarterly reviews of utilization and cost per use case to adjust the mix.
Typical budget ranges
For mid‑market private AI implementations that prioritize internal copilots and RAG:
- Initial hardware and infrastructure uplift:
- Low end (GPU‑light, small scale): 250k–600k.
- Mid range (multi‑function, multiple GPUs): 600k–1.5M.
- High end (regional clusters, training‑capable): 1.5M–3M+.
These ranges assume a blend of on‑prem infrastructure upgrades, networking, storage, and some use of cloud GPU resources. AgenixHub’s experience is that careful workload scoping and quantization of models can reduce compute requirements by 30–50% versus naive sizing.
Infrastructure patterns AgenixHub typically deploys
For mid‑market B2B organizations, AgenixHub commonly implements:
- Core pattern:
- Private AI gateway.
- GPU node pool for LLM inference.
- CPU node pool for retrieval, ETL, and orchestration.
- Vector DB on NVMe storage.
- Kubernetes with observability and security integrations.
- Scalability pattern:
- Horizontal scaling of GPU and CPU nodes via Kubernetes autoscalers.
- Hybrid bursting to cloud GPUs when utilization peaks.
- Storage tiering (NVMe cache plus object storage).
- Resilience pattern:
- At least N+1 redundancy for AI nodes.
- Multi‑AZ or multi‑site clusters for critical workloads.
- Automated backups and disaster‑recovery runbooks for models, indexes, and configuration.
AgenixHub offers commitment‑free consultations to help mid‑market firms assess current infrastructure, estimate realistic GPU/CPU, storage, and network requirements, and choose between on‑prem, colocation, and hybrid options aligned with their regulatory and budget constraints.
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