GUIDE

GPU Memory Is a Product Constraint, Not Just an Infrastructure Number

How model size, precision, context and concurrency change what a deployment can actually serve.

GPU memoryLLM inferencemodel deployment

Why this matters

The same model can be affordable for batch jobs and impractical for interactive traffic. Capacity planning must start from the workload.

The practical takeaways

  • Estimate weights, KV cache and runtime overhead separately.
  • Plan for concurrency, not only a single request.
  • Compare quantization trade-offs with quality tests.

How to apply it

Start with one measurable workflow, define the failure boundary, and publish the result with enough context for another builder to reproduce the decision. The goal is not to chase every announcement; it is to turn useful changes into better products, skills and deployment practice.

Editorial note

This is an original FDE editorial synthesis based on the linked source. It is not a translation or reproduction of the source article.