What are the hardware requirements for SWE Dev 32B?

SWE Dev 32B requires approximately 65.8 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SWE Dev 32B and how large is it?

SWE Dev 32B is developed by THUDM, a model with 32b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate SWE Dev 32B?

SWE Dev 32B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SWE Dev 32B by THUDM — VRAM 65.8GB, 32K context

Name: SWE Dev 32B
Author: THUDM

SWE Dev 32B is an open-source language model by THUDM. Features: 32b LLM, VRAM: 65.8GB, Context: 32K, License: mit, Instruction-Based, Code Generating, LLM Explorer Score: 0.18.

Arxiv:2506.07636 Autotrain compatible Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-coder-... Codegen Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/THUDM/SWE-Dev-32B

SWE Dev 32B Parameters and Internals

LLM Name	SWE Dev 32B
Repository 🤗	https://huggingface.co/THUDM/SWE-Dev-32B
Base Model(s)	Qwen/Qwen2.5-Coder-32B-Instruct Qwen/Qwen2.5-Coder-32B-Instruct
Model Size	32b
Required VRAM	65.8 GB
Updated	2025-07-28
Maintainer	THUDM
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-14 4.9 GB: 2-of-14 4.9 GB: 3-of-14 4.9 GB: 4-of-14 4.9 GB: 5-of-14 4.9 GB: 6-of-14 4.9 GB: 7-of-14 4.9 GB: 8-of-14 4.9 GB: 9-of-14 4.9 GB: 10-of-14 4.9 GB: 11-of-14 4.9 GB: 12-of-14 4.9 GB: 13-of-14 2.1 GB: 14-of-14
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.46.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the SWE Dev 32B capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to SWE Dev 32B

Best Alternatives	Context / RAM	Downloads	Likes
...y Qwen2.5coder 32B V24.1q 200K	195K / 65.8 GB	9	2
Qwen2.5 32B YOYO MIX	128K / 65.7 GB	8	2
Qwen2.5 Coder 32B YOYO	128K / 65.8 GB	8	2
QwQ Qwen2.5 Coder Instruct 32B	128K / 65.8 GB	43	0
QwQenSeek Coder	128K / 65.7 GB	25	5
Rombos Coder V2.5 Qwen 32B	128K / 65.8 GB	10	19
Tessa T1 32B	117K / 65.8 GB	12	22
UIGEN T1.5 32B	117K / 65.8 GB	12	13
Qwen2.5 Coder 32B Instruct	32K / 65.8 GB	1265226	2087
Qwen2.5 Coder 32B Instruct	32K / 65.8 GB	2100	7

Note: green Score (e.g. "73.2") means that the model is better than THUDM/SWE-Dev-32B.