Qwen 2.5 14B Instruct

Overview

Alibaba's Apache 2.0 dense 14B hitting MMLU 79.7 and HumanEval 83.5 across 29+ languages. The pragmatic sweet spot for self-hosted general-purpose chat.

When to pick this model

• General-purpose chat on a single 16–24GB GPU
• Multilingual production workloads needing a permissive license
• RAG pipelines balancing quality and inference cost
• Replacing 7B models that hit a quality ceiling

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	9 GB
Q5_K_M	11 GB
Q8_0	16 GB
FP16 (no quantization)	28 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
MMLU	79.7
HumanEval	83.5
GSM8K	83.1

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• Apache 2.0 — fully commercial-friendly
• MMLU 79.7 and HumanEval 83.5 at 14B scale
• Excellent VRAM-to-quality ratio
• 131k context via YaRN extension

Limitations

• Native context is 32k — 131k requires YaRN configuration
• Outscored on hard reasoning by 30B+ alternatives
• Vision not included — pick Qwen2.5-VL if you need it

Architecture & training

Architecture: Dense 14B · GQA · 131k ctx

Training: 29+ languages.

Quick start

ollama run qwen2.5:14b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.