Qwen 2.5 32B

Overview

Alibaba's Qwen 2.5 32B, the open-weight 32B reference of late 2024 — matching 70B-class quality on most benchmarks at half the VRAM.

When to pick this model

• Self-hosted assistants on a single 24GB GPU at lower precision
• Long-context reasoning workloads up to 128k tokens
• Math and code-heavy pipelines
• Commercial deployments needing Apache 2.0
• A 70B alternative when VRAM is tight

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	19 GB
Q5_K_M	23 GB
Q8_0	35 GB
FP16 (no quantization)	64 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	83.3
HumanEval	90.2
MATH	83.1

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

Strengths

• Quality on par with many 70B models
• 128k context
• Apache 2.0 license
• Strong math, code, and reasoning

Limitations

• Needs ~19GB VRAM at Q4 — pushes the limits of a single 24GB card
• Outperformed by Qwen 3 32B in 2025
• No native vision

Architecture & training

Architecture: Dense Transformer · 64 layers · GQA · Qwen 2.5

Training: 18T tokens. Strong in reasoning, code, and long instructions.

Quick start

ollama run qwen2.5:32b

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