Inference vs. Training: What Your Company Actually Needs

Sizing AI infrastructure properly? WZ-IT plans and operates inference environments on your hardware, in the AI Cube or in an EU cloud - including monitoring and cost control. See the operations layer

Anyone considering their own AI hears two terms that are constantly confused - and the confusion gets expensive: those who believe they need training plan clusters; those who only need inference often manage with a single GPU. As of July 2026.

Table of contents

• The difference in one picture
• Why most companies need no training
• When fine-tuning does pay off
• Sizing hardware correctly
• The cost calculation

The difference in one picture

Training creates or changes a model's weights: massive data, GPU clusters, days to weeks of compute. That is what model makers like Mistral or Meta do. Inference uses the finished model: a request goes in, an answer comes out - permanently, latency-critical, and it drives your running costs.

Why most companies need no training

The most common fallacy: "The model should know our data, so we must train it." In most cases RAG is the right answer - the model stays unchanged and receives relevant documents at query time. That is cheaper, instantly updatable, and lets you enforce permissions technically - which trained knowledge never can: whatever sits in the model can be queried by anyone allowed to use it.

When fine-tuning does pay off

There are legitimate cases: a specific writing style, a domain language with its own terminology, structured output formats. Even then almost always as a LoRA adapter instead of full training - a small overlay is trained, the base model stays untouched, and the hardware requirement stays manageable. The order still holds: exhaust RAG first, then assess fine-tuning.

Sizing hardware correctly

For inference, VRAM matters most - the (quantized) model must fit into memory, plus room for context:

• up to 8B parameters (internal assistants, classification): one consumer GPU or L4, 16-24 GB VRAM
• up to 70B quantized (demanding assistance): 48-80 GB, one large card or two mid-size
• details: GPU and VRAM sizing for LLMs

Serving software like vLLM extracts a multiple of the throughput from the same hardware compared with naive setups - the software choice is a cost lever, not a detail.

The cost calculation

Own inference has largely fixed costs: GPU server, power, operations - regardless of whether 10 or 200 employees ask. APIs scale per token: cheap for sporadic use, expensive under sustained load. Break-even depends on volume and privacy requirements; a gateway like LiteLLM makes both paths usable in parallel and costs visible per team. Assessing which path pays off belongs in every intro call - both paths compared transparently, not as a matter of faith.