On-prem AI stack review

Free 30-minute on-prem AI sanity check with a Zylon engineer

If you’re building private, on-premise AI for a regulated environment, the hard part isn’t the model—it’s the infrastructure choices. Get clear answers on GPUs, model selection, architecture, and governance before you commit budget.

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ON PREMISE AI

The decisions you’re facing

Four choices drive cost, latency, reliability, and auditability

GPUs & server architecture

Right-size GPU memory, networking, storage, and redundancy for real workloads—not pilot demos.

Procurement guidance
Topologies
Throughput planning

Model selection & latency trade-offS

Pick models that meet latency targets while preserving capability, safety, and cost predictability

Quantization
Routing
Context strategy

AI stack for multiple use cases

Design the platform layer: ingestion, vector search, RAG/agents, connectors, and environments for teams.

RAG patterns
Eval loop
Multi-tenant setup

Governance, monitoring, and observability

Enforce access controls, audit logs, and usage monitoring so security and compliance aren’t an afterthought

RBAC
Audit trails
Rate limits

THE ZYLON DIFFERENCE

What you’ll get in the session

Practical answers tailored to your environment, no generic consulting deck.

A clear recommendation on GPU + server architecture for your constraints
A model shortlist with latency/cost trade-offs
A minimal on-prem AI stack blueprint (ingestion → retrieval/agents → serving)
A governance checklist (RBAC, audit logging, rate limits, data boundaries)
Risks to avoid before you commit budget

No sales agenda or obligation. Some teams deploy Zylon, others build internally.

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