Advanced On-Premises LLM Infrastructure
Advanced On-Premises LLM Infrastructure
By the end of this course, students will design, deploy, and operate a private LLM serving platform on their own hardware — sizing GPU capacity for a real workload, running multi-GPU inference with vLLM, exposing OpenAI-compatible endpoints behind authentication, quotas, and rate limits, and proving performance with TTFT, inter-token latency, and P99 benchmarks. They will find bottlenecks, set SLOs, and recover from failures like infrastructure engineers, not model users.
What you'll learn
Size an LLM deployment from workload requirements — GPU memory math for weights, KV cache and overhead, concurrency vs context-length trade-offs, and the PCIe, NVLink, CPU, RAM, storage and network implications of each choice- Architect a private LLM platform with separated control, inference, and storage planes — across bare-metal, containerized, private-cloud, and air-gapped deployments, with explicit failure domains and high availability
- Run multi-GPU inference with vLLM — tensor, pipeline, and data parallelism, continuous batching, request scheduling, and KV-cache management — and know which knob to turn for which bottleneck
- Get more throughput from the same hardware using prefix caching, speculative decoding, and quantization — measuring quality vs latency vs memory at every step
- Build the serving layer: OpenAI-compatible gateway, request routing, multi-model and LoRA adapter serving, workload isolation, and GPU allocation
- Operate a shared platform safely — authentication, quotas, rate limits, multi-tenancy, replica management, controlled rollout, and restricted-network deployment
- Benchmark and defend performance with load testing, saturation analysis, and GPU telemetry — then turn it into capacity forecasts, admission control, SLOs, alerting, failure drills, and a rollback and recovery runbook
This course includes:
Course requirements
Familiar of MLOps basics Reference: https://zomra.io/courses/the-mlops-practitioner - Production MLOps experience — The MLOps Practitioner or equivalent. Containers, REST APIs, CI/CD, and basic monitoring are assumed as prerequisites, not taught
- Comfortable on the Linux command line and in Python — you can write a Dockerfile, edit YAML, and debug a running service from its logs
- Access to at least one NVIDIA GPU (a rented cloud instance is fine). Two or more GPUs are recommended for the multi-GPU labs
- Basic familiarity with LLM concepts — tokens, context windows, prompt and completion. No training or fine-tuning experience needed
Description
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
Who is this course for
MLOps and ML engineers who can ship models to production but hit a wall with LLMs — where a single model doesn't fit on one GPU, throughput depends on scheduler internals, and the usual serving playbook stops working
Platform, infrastructure, and DevOps engineers asked to stand up a private LLM platform for their organization, who need to size the hardware, run it multi-tenant, and put an SLO on it
AI engineers and technical leads at organizations that can't send data to an API — banks, healthcare, government, telecom, defense — where on-premises or air-gapped is a requirement, not a preference, and someone has to own the infrastructure decisions
Who is this course for
MLOps and ML engineers who can ship models to production but hit a wall with LLMs — where a single model doesn't fit on one GPU, throughput depends on scheduler internals, and the usual serving playbook stops working
Platform, infrastructure, and DevOps engineers asked to stand up a private LLM platform for their organization, who need to size the hardware, run it multi-tenant, and put an SLO on it
AI engineers and technical leads at organizations that can't send data to an API — banks, healthcare, government, telecom, defense — where on-premises or air-gapped is a requirement, not a preference, and someone has to own the infrastructure decisions
Course syllabus
5 Weeks • 5 Modules • 5 lessons (4 live lessons)
Most teams buy GPUs before they know what workload they're serving, then discover the box can't hold the KV cache at their target concurrency. This module starts from the workload and works backwards to the hardware — so every architectural decision has a number behind it.
Module content:
Lesson 1 — Workload and model-sizing methodology
Requests per second, prompt and completion length, concurrency targets. Translating them into GPU memory: weights, KV cache, activation overhead, and the headroom you actually need.
Lesson 2 — Compute-capacity planning
Memory bandwidth vs FLOPs, which one bounds your workload, and how to tell before you buy.
Lesson 3 — The physical layer
PCIe topology, NVLink, CPU and RAM sizing, storage throughput for model loading, and network considerations for multi-node.
Lesson 4 — Plane separation
Control plane, inference plane, and storage as distinct concerns — why collapsing them creates outages you can't debug.
Lesson 5 — Deployment topologies
Bare-metal, containerized, private-cloud, and air-gapped architectures: what each one costs you in operational complexity, and when regulation forces your hand.
Lesson 6 — High availability and failure domains
Mapping single points of failure across GPU, node, rack, and network. What "highly available" means when a model takes four minutes to load.
Lab: Design and validate an on-premises LLM architecture for a given workload spec, and defend the sizing.
Private LLM Infrastructure Architecture
Frequently Asked Questions
If you still have questions, contact instructor or Zomra on LinkedIn
All live sessions are recorded and available for replay within 24 hours. You can watch them at your convenience.
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Certificate of Completion
Advanced On-Premises LLM Infrastructure
Graduated from Advanced On-Premises LLM Infrastructure at Zomra, with strong grasp of its fundamentals, completed all coursework and activities.