Practical guidance for complex infrastructure decisions.

Private AI starts with requirements—not a model name.

The model receives most of the attention, but the hardest decisions usually live around it. Organizations must define what data can be processed, where inference may occur, how users authenticate, what gets logged, how models are approved, and what performance is actually required.

Start with four questions

• Which business or mission workflow is being improved?
• What data classifications and boundaries apply?
• What latency, concurrency, and availability are required?
• Who will operate, secure, and update the platform?

Those answers shape GPU capacity, deployment topology, model-serving software, network controls, storage, orchestration, and the user interface. A smaller, well-operated system frequently creates more value than a larger platform assembled without an operating model.

Ollama and vLLM solve different operational problems.

Ollama offers a straightforward experience for downloading and running models, making it useful for individual users, laboratories, and smaller controlled deployments. vLLM is designed around high-throughput model serving and becomes more compelling when many users share the same model.

The practical distinction

For a single-user workstation, simplicity may matter more than aggregate throughput. For an enterprise service, continuous batching, efficient KV-cache management, API compatibility, observability, scaling, and predictable concurrency become more important.

The right choice depends on the service being delivered—not which engine appears higher on a feature list.

GPU memory is only the first constraint.

Model weights, precision, context length, concurrent requests, KV cache, framework overhead, and runtime behavior all consume resources differently. A model that loads successfully is not necessarily a model that can support the intended user population.

Capacity planning should test

• Representative prompt and output lengths
• Concurrent-user behavior
• Time to first token and total response time
• Memory utilization during sustained load
• Failure and recovery behavior

Prototype results should be translated into an operating envelope: supported models, maximum contexts, concurrency targets, response objectives, and growth thresholds.