Daily Local LLM Benchmark Snapshot: Decisions You Can Use (2026)

Daily field report for local inference decisions: verified throughput anchors, VRAM boundary guidance, and local-vs-cloud fallback triggers.

Published: 2026-03-22 Updated: 2026-03-22 Intent: benchmark

What changed today

This update consolidates the latest verified local inference measurements and turns them into practical deployment decisions.

Verified benchmark anchors

  • gpt-oss:20b: 166.0 tok/s | latency 1256 ms | test 2026-03-15T12:17:40Z
  • qwen3-coder:30b: 159.9 tok/s | latency 999 ms | test 2026-03-11T04:17:51Z
  • qwen3:8b: 137.2 tok/s | latency 1488 ms | test 2026-03-15T12:17:40Z
  • ministral-3:14b: 89.3 tok/s | latency 2087 ms | test 2026-03-15T12:17:40Z
  • qwen2.5:14b: 84.9 tok/s | latency 975 ms | test 2026-03-11T04:17:51Z

Decision guide

  1. If your target model fits VRAM with headroom, prioritize local for predictable latency and lower long-run cost.
  2. If p95 latency or throughput misses production target, keep local as baseline and burst to cloud only for peak windows.
  3. If failure rate rises (OOM/retry spikes), step down quantization or reduce concurrent load before scaling out.

Operational checklist

  • Validate tokens/s and latency under representative prompt length.
  • Track OOM and retry counts by model and quantization level.
  • Recalculate break-even weekly for local hardware vs cloud rental.

Next actions

  • Estimate fit: /en/tools/vram-calculator/
  • Hardware path: /en/affiliate/hardware-upgrade/
  • Cloud fallback: /go/runpod and /go/vast

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