Today's Local LLM Pick: ministral-3:14b on RTX 3090 (2026)

Fast verdict

ministral-3:14b is a moderate-speed general-purpose model on a 24GB RTX 3090 (80.7 tok/s). It is worth testing locally for batch or offline workloads. For real-time interactive use, measure end-to-end latency with your typical prompt length before committing.

ministral-3:14b fits comfortably in 24GB at standard quantizations. Monitor VRAM usage if you push context beyond 8K tokens. It ranks #5 of 18 in throughput among currently measured models on this RTX 3090. The next faster model is qwen2.5:14b (84.0 tok/s, 4% faster). The next slower model is deepseek-r1:14b (76.6 tok/s, 5% slower).

The daily goal is simple: help a 3090 owner decide what to download tonight, what to skip, and when a cloud fallback is the better use of time.

Today’s pick

Model: ministral-3:14b
Category: general-purpose
Size tier: medium
Performance tier: moderate
RTX 3090 speed: 80.7 tok/s
Latency: 2003 ms
Test time: 2026-07-08T15:58:01Z
Baseline command:

ollama run ministral-3:14b

Who should try it

RTX 3090 owners deciding whether to download ministral-3:14b tonight for local experimentation.
Users comparing local inference speed against cloud rental (RunPod, Vast) before committing to a workflow.
Anyone building a local LLM toolbox who wants a verified baseline for this model.

Who should skip it

Users who need long-context production stability before a sustained run has been verified.
Teams whose workload requires predictable p95 latency under concurrency.
8GB/12GB GPU owners unless a smaller quantized variant exists.

Watch points

Workload-specific testing: generic benchmarks do not guarantee performance on your particular use case.
Context length: always test at your target context length before assuming production readiness.
Quantization trade-off: lower quantization saves VRAM but may reduce output quality on nuanced tasks.

Verified benchmark anchors

gpt-oss:20b: 156.1 tok/s | latency 1524 ms | test 2026-04-29T05:39:58Z
qwen3-coder:30b: 149.8 tok/s | latency 897 ms | test 2026-07-08T15:58:01Z
qwen3:8b: 112.7 tok/s | latency 1536 ms | test 2026-07-08T15:58:01Z
qwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Z
ministral-3:14b: 80.7 tok/s | latency 2003 ms | test 2026-07-08T15:58:01Z

RTX 3090 decision guide

Batch is the sweet spot: ministral-3:14b is best for offline/batch jobs where throughput matters more than single-shot latency.
Test at your context length: moderate-speed models can slow significantly at longer contexts.
Quantization choice matters: stepping from Q8 to Q4 gains speed but test quality degradation first.
Cloud fallback plan: if local latency misses your target, use RunPod/Vast for time-sensitive runs.

Comparisons to validate

ministral-3:14b vs the next-fastest and next-slowest model in the benchmark feed.
ministral-3:14b vs qwen3:8b — same size tier, 81 vs 113 tok/s.
ministral-3:14b local power cost vs A100 rental for the same workload.

Next actions

Estimate VRAM fit: /en/tools/vram-calculator/
Model page: /en/models/ministral-3-14b-q4/
Benchmark changelog: /en/benchmarks/changelog/
Local 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.