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

Fast verdict

ministral-3:14b is a heavy model on 24GB VRAM (7.4 tok/s). It is best suited for offline batch processing, proof-of-concept validation, or cloud fallback scenarios. Reduce context or step down quantization before attempting interactive use.

ministral-3:14b fits comfortably in 24GB at standard quantizations. Monitor VRAM usage if you push context beyond 8K tokens. It ranks #11 of 18 in throughput among currently measured models on this RTX 3090. The next faster model is deepseek-r1:14b (7.5 tok/s, 1% faster). The next slower model is llama4:16x17b (5.6 tok/s, 32% 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: heavy
• RTX 3090 speed: 7.4 tok/s
• Latency: 18155 ms
• Test time: 2026-06-24T06:22:37Z
• 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
• qwen2.5:14b: 84.0 tok/s | latency 946 ms | test 2026-04-29T05:39:58Z
• nemotron-3-nano:30b: 57.0 tok/s | latency 2468 ms | test 2026-04-01T11:53:50Z
• translategemma:27b: 41.3 tok/s | latency 3142 ms | test 2026-04-01T11:53:50Z
• qwen3-coder:30b: 24.9 tok/s | latency 3724 ms | test 2026-06-24T06:22:37Z

RTX 3090 decision guide

1. Cloud may win: at 7.4 tok/s on 24GB, ministral-3:14b may be more cost-effective on RunPod or Vast.
2. Reduce aggressively: step down to Q4 or IQ4 and minimize context to fit VRAM.
3. Offline only: do not rely on this model for interactive or real-time local workloads.
4. Hardware path: if you run models this size daily, consider multi-GPU or cloud as a permanent solution.

Comparisons to validate

• ministral-3:14b vs the next-fastest and next-slowest model in the benchmark feed.
• ministral-3:14b vs qwen2.5:14b — same size tier, 7 vs 84 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

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