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Qwen2.5-32B

Parameters

32B

Context Length

131K

Modality

Text

Architecture

Dense

License

Apache 2.0

Release Date

19 Sept 2024

Knowledge Cutoff

Jun 2024

API Pricing (per 1M)

Input: $0.87 · Output: $0.87

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

64.33 GB VRAM

Consumer

3x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

131,072 tokens

95.58 GB VRAM

Consumer

5x RTX 4090

24GB VRAM

Datacenter

2x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: RoPEHidden: 8.2k · Context: 131K · Vocab: 152.1kx 60 layersRMSNormPre-AttentionGrouped-Query Attention96Q / 8KV headsHead dim: 85+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 27.6k+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#173

BenchmarkScoreRank

Software Engineering

SWE-bench Verified

0.38

33

Professional Knowledge

MMLU Pro

0.69

45

Graduate-Level QA

GPQA

0.495

104

Intelligence Index

Artificial Analysis

0.07

281

General Knowledge

Reference
MMLU

0.833

16

Coding

Archived
Aider Coding

0.16

19

Rankings

Overall Rank

#173

Coding Rank

#138

About Qwen2.5-32B

Qwen2.5-32B is a high-capacity dense transformer from Alibaba Cloud engineered for complex reasoning, long-form content creation, and JSON data parsing. It delivers high stability across diverse system prompts over a 131K token context window.

Technical Specifications

Attention

Attention Structure

Grouped-Query Attention

Attention Heads

96

Key-Value Heads

8

Position Embedding

ROPE

RoPE Theta

1,000,000

Sliding Window Attention

No

Sliding Window Size

131,072

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

8,192

Number of Layers

60

FFN Intermediate Size (Dense)

27,648

Tokenizer

Vocabulary Size

152,064

About Qwen2.5

Qwen2.5 by Alibaba is a family of dense, decoder-only language models available in various sizes, with some variants utilizing Mixture-of-Experts. These models are pretrained on large-scale datasets, supporting extended context lengths and multilingual communication. The family includes specialized models for coding, mathematics, and multimodal tasks, such as vision and audio processing.


Other Qwen2.5 Models
Qwen2.5-32B - VRAM Requirements, Specs & Benchmarks