Qwen 2.5 32B
By Alibaba · China
Updated 2026-07-13
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.
In practice, Qwen 2.5 32B wants a 24 GB card at Q4_K_M (19 GB). Stepping up to Q8_0 nearly doubles the footprint to 35 GB, and unquantized FP16 weights take 64 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Qwen 2.5 32B needs roughly 32 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Qwen 2.5 32B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Qwen 2.5 32B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 19 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 19 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 19 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q5_K_M (23 GB used) |
| 32 GB | RTX 5090 | Q5_K_M (23 GB used) |
Which GPU should you buy to run Qwen 2.5 32B?
To run Qwen 2.5 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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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.
To put Qwen 2.5 32B in context: its MMLU score of 83.3 ranks #7 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 90.2 ranks #2 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
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
Typical workloads
In our catalog grid, Qwen 2.5 32B is filed under Reasoning, Advanced Generalist — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense Transformer · 64 layers · GQA · Qwen 2.5
Training: 18T tokens. Strong in reasoning, code, and long instructions.
A landmark open-weight 32B that's still a strong default — upgrade to Qwen 3 32B when you can.
Quick start
ollama run qwen2.5:32bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Qwen 2.5 32B need?
At the recommended Q4_K_M quantization, Qwen 2.5 32B needs about 19 GB of VRAM. Q8_0 takes 35 GB, and unquantized FP16 weights take 64 GB.
Can Qwen 2.5 32B run without a GPU?
Yes — with roughly 32 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Qwen 2.5 32B support?
Qwen 2.5 32B supports a 128k-token context window (131,072 tokens).
Can I use Qwen 2.5 32B commercially?
Yes. Qwen 2.5 32B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Qwen 2.5 32B on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Qwen 2.5 32B should I download first?
Start with Q4_K_M (19 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.