What are the hardware requirements for Llama 3.1 Minitron 4B Depth Base?

Llama 3.1 Minitron 4B Depth Base requires approximately 9.1 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3.1 Minitron 4B Depth Base and how large is it?

Llama 3.1 Minitron 4B Depth Base is developed by nvidia, a model with 4b 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 Llama 3.1 Minitron 4B Depth Base?

Llama 3.1 Minitron 4B Depth Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.1 Minitron 4B Depth Base by nvidia — VRAM 9.1GB, 128K context

Name: Llama 3.1 Minitron 4B Depth Base
Author: nvidia

Llama 3.1 Minitron 4B Depth Base is an open-source language model by nvidia. Features: 4b LLM, VRAM: 9.1GB, Context: 128K, License: other, LLM Explorer Score: 0.18.

Arxiv:2009.03300 Arxiv:2407.14679 Arxiv:2408.11796 En Endpoints compatible Llama Llama-3 Nemo Nvidia Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Depth-Base

Llama 3.1 Minitron 4B Depth Base Parameters and Internals

Model Type

text-to-text

Use Cases

Areas:

commercial, natural language generation tasks

Applications:

code generation, language understanding

Primary Use Cases:

5-shot performance, zero-shot performance, code generation

Limitations:

Model trained on potentially biased or toxic data, May amplify biases or return toxic responses

Considerations:

Developers should ensure model meets their industry's requirements

Supported Languages

English (Proficient), Multilingual (Proficient)

Training Details

Data Sources:

webpages, dialogue, articles, written materials

Data Volume:

94 billion tokens

Methodology:

Knowledge Distillation

Training Time:

July 29, 2024 - Aug 3, 2024

Model Architecture:

Transformer Decoder (Auto-Regressive Language Model)

Responsible Ai Considerations

Fairness:

The model may contain societal biases.

Transparency:

Not explicitly stated

Accountability:

Recommended to be managed by user's internal model team

Mitigation Strategies:

Policies to develop Trustworthy AI, report vulnerabilities

Input Output

Input Format:

String

Accepted Modalities:

Text

Output Format:

String

Performance Tips:

Works well within 8k characters or less

LLM Name	Llama 3.1 Minitron 4B Depth Base
Repository 🤗	https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Depth-Base
Model Size	4b
Required VRAM	9.1 GB
Updated	2026-07-29
Maintainer	nvidia
Model Type	llama
Model Files	5.0 GB: 1-of-2 4.1 GB: 2-of-2
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	other
Context Length	131072
Model Max Length	131072
Transformers Version	4.43.3
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Llama 3.1 Minitron 4B Depth Base 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 Llama 3.1 Minitron 4B Depth Base

Best Alternatives	Context / RAM	Downloads	Likes
4Bcpt	256K / 8.8 GB	5	0
HoldMy4BKTO	256K / 8.8 GB	5	0
Xgen Small 4B Instruct R	256K / 17.7 GB	54	4
Xgen Small 4B Base R	256K / 17.7 GB	30	3
SJT 4B	146K / 7.6 GB	7	0
...lama 3.1 Nemotron Nano 4B V1.1	128K / 9 GB	3940	116
Nemotron W 4b MagLight 0.1	128K / 9.2 GB	9	3
Loxa 4B	128K / 16 GB	8	0
Nemotron W 4b Halo 0.1	128K / 9.2 GB	22	3
Aura 4B	128K / 9 GB	44	14

Note: green Score (e.g. "73.2") means that the model is better than nvidia/Llama-3.1-Minitron-4B-Depth-Base.