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.
What changed today
This update consolidates the latest verified local inference measurements and turns them into practical deployment decisions.
Verified benchmark anchors
qwen3-coder:30b: 160.0 tok/s | latency 835 ms | test 2026-04-29T05:39:58Zgpt-oss:20b: 156.1 tok/s | latency 1524 ms | test 2026-04-29T05:39:58Zqwen3:8b: 135.3 tok/s | latency 1270 ms | test 2026-04-29T05:39:58Zministral-3:14b: 87.3 tok/s | latency 2253 ms | test 2026-04-29T05:39:58Zqwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Z
Decision guide
- If your target model fits VRAM with headroom, prioritize local for predictable latency and lower long-run cost.
- If p95 latency or throughput misses production target, keep local as baseline and burst to cloud only for peak windows.
- 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
Affiliate Disclosure: This post may include affiliate links. LocalVRAM may earn a commission at no extra cost.
Related model pages
Model profiles referenced by this article, with VRAM fit and measured or estimated status.
- Ministral 3 14B Q4 ministral-3:14b · Q4 · 12-14GB VRAM · measured
- GPT-OSS 20B Q4 gpt-oss:20b · Q4 · 16-20GB VRAM · estimated
- Qwen3 8B Q4 qwen3:8b · Q4 · 8-10GB VRAM · estimated
- Qwen3 Coder 30B Q4 qwen3-coder:30b · Q4 · 20-22GB VRAM · estimated
- Qwen2.5 14B Q4 qwen2.5:14b · Q4 · 10-20GB VRAM · estimated