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Use cases
Use case3 minUpdated 2026-07-04

Local inference

Benchmark open models locally before renting GPUs, then promote only models that pass quality, latency, and utilization tests.

Audience

Developers and platform teams

Metric

Benchmark first

Sources

3 official links

Best default

Local inference is the fastest way to test whether a routine workload can leave frontier APIs. It should start as a benchmark lane before becoming production infrastructure.

  • Run a candidate model on local hardware with real prompts and expected output length.
  • Measure acceptance rate, latency, memory, tokens per second, and fallback volume.
  • Move to Modal, RunPod, Lambda, Vast.ai, Baseten, or another GPU host only after quality is proven.

Avoid

Do not treat free local tokens as free production cost. Operations, monitoring, model updates, storage, egress, and frontier fallback still belong in the plan.