What are the hardware requirements for Deepseek Math 7B Instruct?

Deepseek Math 7B Instruct requires approximately 13.9 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Deepseek Math 7B Instruct and how large is it?

Deepseek Math 7B Instruct is developed by deepseek-ai, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Deepseek Math 7B Instruct perform on standard benchmarks?

Deepseek Math 7B Instruct has the following published scores: MMLU 55.97. Compare against reference models on this page or on the LLM Explorer leaderboards.

Deepseek Math 7B Instruct by deepseek-ai — VRAM 13.9GB, 4K context

Name: Deepseek Math 7B Instruct
Author: deepseek-ai

Deepseek Math 7B Instruct is an open-source language model by deepseek-ai. Features: 7b LLM, VRAM: 13.9GB, Context: 4K, License: other, Instruction-Based, LLM Explorer Score: 0.18, Arc: 53.4, HellaSwag: 71.5, MMLU: 56, GSM8K: 19.4.

Arxiv:2402.03300 Conversational Endpoints compatible Eval-results Instruct Llama Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct

Deepseek Math 7B Instruct Parameters and Internals

Model Type

text generation, math problem-solving

Use Cases

Areas:

research, education

Applications:

solving mathematical problems, providing step-by-step solutions

Primary Use Cases:

math tutoring, academic research

Considerations:

Use chain-of-thought prompts for optimal performance.

Additional Notes

It's recommended to avoid adding a system prompt for compatibility reasons.

Supported Languages

en (high proficiency), zh (high proficiency)

Input Output

Input Format:

Messages structured as JSON objects with user prompts.

Accepted Modalities:

text

Output Format:

Formatted step-by-step solutions with final answer within boxed notation.

Performance Tips:

Use chain-of-thought prompts to improve solution generation.

LLM Name	Deepseek Math 7B Instruct
Repository 🤗	https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct
Model Size	7b
Required VRAM	13.9 GB
Updated	2026-07-30
Maintainer	deepseek-ai
Model Type	llama
Instruction-Based	Yes
Model Files	10.0 GB: 1-of-2 3.9 GB: 2-of-2
Model Architecture	LlamaForCausalLM
License	other
Context Length	4096
Model Max Length	4096
Transformers Version	4.33.1
Tokenizer Class	LlamaTokenizerFast
Beginning of Sentence Token	<｜begin▁of▁sentence｜>
End of Sentence Token	<｜end▁of▁sentence｜>
Vocabulary Size	102400
Torch Data Type	bfloat16

Rank the Deepseek Math 7B Instruct 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

Quantized Models of the Deepseek Math 7B Instruct

Model	Likes	Downloads	VRAM
...epseek Math 7B Instruct AWQ Q4	0	6	4 GB
Deepseek Math 7B Instruct GPTQ	0	5	4 GB
Deepseek Math 7B Instruct GGUF	1	977	2 GB

Best Alternatives to Deepseek Math 7B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
124	1024K / 16.1 GB	93	0
A5.4	1024K / 16.1 GB	12	0
A3.4	1024K / 16.1 GB	13	0
A2.4	1024K / 16.1 GB	12	0
... Qwen2.5llamaify 7B V23.1 200K	195K / 15.2 GB	29	5
Salamandra 7B Instruct 2606	160K / 15.6 GB	9217	3
SuperNeuralDreadDevil 8B	128K / 16.1 GB	46	1
Falcon3 7B Instruct	32K / 14.8 GB	11749	80
...truct Auto Round Int4 Gs64 Sym	32K / 6.9 GB	48	1
Falcon3 Jessi V0.4 7B Slerp	32K / 14.9 GB	34	9

Note: green Score (e.g. "73.2") means that the model is better than deepseek-ai/deepseek-math-7b-instruct.