LOCKEDIN / FORWARD-DEPLOYED ENGINEERING 101
Understand it.
Then build it.
You can be an experienced engineer and still be new to agents. Start with what each thing is and how it helps a person do useful work. Explore 48 original introductions across software, agents, cloud, security and delivery. Start here without a model key; each topic connects to deeper instruction and practice.
- 101UnderstandPlain language, visual walkthrough, vocabulary
- 102Build with guidanceOriginal lessons, examples and knowledge checks
- 103Practise and explainRunnable labs, failure cases and handover
- 01Choose the model and runtime
- 02Budget memory
- 03Measure and operate
Model serving and capacity engineering
What does running a private LLM involve?
Private inference runs a model within an environment whose access and data paths you control. You need model weights, a serving runtime, compatible hardware, identity, monitoring and an operating team. NVIDIA Nemotron names a model family; NVIDIA NIM provides inference microservices. They are different layers.
Some enterprise constraints call for controlled inference placement, and an FDE must explain the operational tradeoff.
CONNECT IT TO WHAT YOU KNOW
Start with something familiar.
Owning the engine means operating the engine room: capacity, maintenance, access and recovery become part of your responsibility.
Private placement does not automatically establish privacy or compliance. Check model licensing, data flows, telemetry, storage and network access. Hardware sizing needs the actual model architecture and workload.
Words worth knowing
- Weights
- The learned parameters of a model.
- KV cache
- Stored attention keys and values used during generation.
- Quantization
- Representing model values with reduced precision, with quality and compatibility tradeoffs.
ONE EXAMPLE / FICTIONAL CASE
Watch the work take shape.
A team wants a maintenance assistant to use an approved model inside its environment with bounded simultaneous requests.
- 01Choose the model and runtime
- 02Budget memory
- 03Measure and operate
Choose the model and runtime
Verify the license, supported hardware and intended task behavior before selecting a serving package.
A compatible deployment candidateFROM RECOGNITION TO REASONING
Use the existing synthetic memory-budget explorer.
- 01
Change request concurrency and output allowance.
- 02
Explain why the same GPU capacity admits one setting and refuses another.
- 03
List the real measurements still needed before hardware selection.
A capacity argument that distinguishes estimates from benchmarks.
Compare your reasoning when you are ready
The explorer's fixed profile teaches reservation arithmetic. Real capacity depends on architecture, precision, runtime and workload, so its result cannot be advertised as measured Nemotron performance.
Keep notes in your own working copy. Moving through these cards does not record completion.
TURN THE IDEA INTO USEFUL WORK
You can now go deeper.
Private placement does not automatically establish privacy or compliance. Check model licensing, data flows, telemetry, storage and network access. Hardware sizing needs the actual model architecture and workload.
Academy lessons use your existing training access. Free guides stay open.