Advanced On-Premises LLM Infrastructure

Your company just decided it can't send customer data to OpenAI. Now you own the alternative.

That mandate is landing on engineers across the region — data residency rules, a regulator, or a security review that came back with a no. And it arrives with questions nobody trained you for. How many GPUs? Will a 70B model fit? What happens at 200 concurrent users? Why is it fast in testing and unusable in production? Why did adding a second GPU make throughput worse?

This course answers those questions with numbers instead of guesses.

Over four four-hour sessions you'll build a private LLM serving platform from the hardware up, then prove it works under load. Not a demo running on one GPU — a multi-GPU, multi-tenant endpoint with authentication, quotas, benchmarks, and a recovery runbook. The kind of thing you can hand to another team and defend in a production readiness review.

What the month covers

Sizing before spending. Start from the workload — requests per second, prompt and completion lengths, target concurrency — and work backwards to GPU memory: weights, KV cache, activation overhead, and the headroom you actually need. Whether memory bandwidth or compute bounds your workload, and how to know before the purchase order goes out.

The architecture. Separating control, inference, and storage planes, and why collapsing them creates outages you can't debug. Bare-metal, containerized, private-cloud, and air-gapped topologies, and what each costs you operationally. PCIe layout and NVLink, and what high availability really means when a model takes four minutes to load.

Multi-GPU inference that's actually tuned. Inside the vLLM runtime: scheduler, paged KV cache, continuous batching, preemption under queue pressure. Tensor, pipeline, and data parallelism — what each splits, what each costs in communication, and how to choose based on model size and interconnect. Prefix caching and speculative decoding where they genuinely help and where they add latency. Quantization and what it really costs you in quality.

From a process to a platform. OpenAI-compatible gateway, request routing, health checks and failover. Multi-model and LoRA adapter serving, with adapter hot-loading. Workload isolation and GPU allocation so one tenant can't starve another. Authentication, quotas, rate limits, controlled rollout, and restricted-network deployment.

Proving it works. Time to first token, inter-token latency, throughput, P95 and P99 — and why averages hide the problem. Load testing, saturation analysis, GPU telemetry, and finding the true bottleneck instead of guessing at it. Capacity forecasting, admission control, SLOs that you can actually meet, alerting, failure drills, model rollback, and infrastructure recovery.

Every session ends with a lab that feeds the applied project, so the platform grows across the month rather than being assembled at the end.

What you'll walk away with

A working on-premises LLM endpoint running multi-GPU or multi-replica, with authentication and resource controls in front of it, a benchmark report and monitoring dashboard behind it, and a runbook another engineer could follow at 3am without calling you. Plus the sizing methodology — the part you'll reuse on every deployment after this one.

Where this sits

This is the advanced track that follows The MLOps Practitioner. Packaging, containers, CI/CD, and basic monitoring are treated as prerequisites, not re-taught — familiar tools come back, but through LLM-specific workloads and operational exercises. If you've completed the Practitioner, this is your next step.
Reference: https://zomra.io/courses/the-mlops-practitioner