LLM GPU sizing

GPU Sizing for On-Prem LLM + RAG

Plan capacity with real inputs: users, concurrency, latency targets, and model choices.

Inputs You Need

  • Active users and peak concurrency
  • Latency target (p95) and response length
  • Model size and context window requirements

Understand Workloads

  • Interactive chat (latency-sensitive)
  • Batch ingestion (CPU/IO-heavy)
  • Evaluation runs and monitoring

Practical Tiering

  • Pilot: one team, limited data scope
  • Team rollout: multiple workflows + SSO
  • Enterprise: HA, multi-department, full governance

Common Sizing Mistakes

  • Ignoring concurrency and peak hours
  • Not accounting for context window growth
  • No headroom for retries and spikes

Sample Scenarios

  • Support team: fast drafts + citations
  • Legal team: long context + comparisons
  • Enterprise search: high retrieval volume

FAQ

Use peak active users and a realistic concurrency ratio (often 5-20% depending on workflow).

RAG adds CPU/IO work; GPU demand is still dominated by LLM inference throughput.

Yes-ingestion/retrieval can be CPU-heavy; inference benefits most from GPUs.

For higher throughput and maintenance flexibility, multiple GPUs or nodes are recommended.

A small tier for one department with a defined document set and clear KPI targets.

Related Pages

NVIDIA GPU AI Server for Enterprise LLM + RAG On-Prem LLM for Private Enterprise AI Pricing & POC for On-Prem LLM + Enterprise RAG Enterprise AI Security, Privacy & Governance

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