Run your AI factory on infrastructure you control
Enterprises running AI on their own GPUs need data delivery, inference routing, separation between teams, and runtime security on one platform.
Private AI is now a production platform
F5's 2026 State of Application Strategy Report found that 78% of enterprises now run AI inference as a core operation. Private AI platforms serve many teams, models, and agents from shared GPUs, and inherit every requirement production apps have: fast data, routing, separation, security, and visibility. F5 delivers those on the F5 Application Delivery and Security Platform.
Keep GPUs fed with data
Load-balanced, resilient S3 data paths for training, fine-tuning, and retrieval.
Route requests to the right model
Inference-aware routing across models and GPUs, built to Gateway API standards.
Share GPUs across teams safely
Separate business units and environments on one GPU pool with network isolation.
Secure prompts, data, and agents
Runtime guardrails and red teaming for internal AI apps, agents, and their APIs.
F5 helps enterprises run AI in production
Deliver data to GPUs
Inference routing
NGINX Gateway Fabric
Gateway API implementation for application and inference traffic in clusters.
Separation between teams
BIG-IP Next for Kubernetes deployed on NVIDIA BlueField DPUs
The same controls with the data path offloaded to the DPU, for larger estates.
Runtime AI security
Resources
Reference architectures
Explore the AI reference architecture
Reports
Solution guides
FAQs
An enterprise AI factory is AI infrastructure an organization runs on GPUs it controls, in its own data centers, colocation, or dedicated cloud capacity, to train, fine-tune, and serve models for its own applications and agents. Organizations build one to keep sensitive data in-house, meet regulatory requirements, control costs at scale, or customize models. It turns AI into a platform the organization operates, with the same delivery and security needs as any production system.
To keep GPUs from waiting on data, treat the storage path like any other production traffic. Put a load balancer in front of S3 endpoints so jobs spread across storage nodes instead of hammering one, fail over without restarting the job when a node or site drops, and steer traffic across sites and clouds by policy. Then protect the S3 APIs, because the data path is also an attack path.
To share GPUs across an enterprise, pool the GPUs and give each team a boundary and a share. Kubernetes namespaces and quotas handle scheduling, network-level isolation keeps team traffic apart, and token-based limits per team stop one group's workload from crowding out the rest. Inference-aware routing then spreads requests by the work they carry, so shared GPUs are used evenly.
To securely run LLMs on premises, treat the model as one component of a production pipeline. Separate teams and environments in the network, control what models and agents can reach outside the cluster, screen prompts and responses at runtime for injection and data leakage, test models and agents adversarially before release, and protect the APIs that expose them. Then log what crossed each boundary.
No, you do not need DPUs for an enterprise AI factory. BIG-IP Next for Kubernetes runs on standard host CPUs and Kubernetes, which is where most enterprise AI platforms start, and F5 data delivery runs on BIG-IP software, VELOS, and rSeries. NVIDIA BlueField DPUs move the networking data path off the host and add hardware-level separation, which becomes worthwhile as the estate grows.