What are the hardware requirements for Mixtral V0.3 Full 16bit?

Mixtral V0.3 Full 16bit requires approximately 14.5 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 Mixtral V0.3 Full 16bit and how large is it?

Mixtral V0.3 Full 16bit is developed by Asit03, a model with 7b 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 Mixtral V0.3 Full 16bit?

Mixtral V0.3 Full 16bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mixtral V0.3 Full 16bit by Asit03 — VRAM 14.5GB, 32K context

Name: Mixtral V0.3 Full 16bit
Author: Asit03

Mixtral V0.3 Full 16bit is an open-source language model by Asit03. Features: 7b LLM, VRAM: 14.5GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.18.

4bit Base model:finetune:unsloth/mi... Base model:unsloth/mistral-7b-... Conversational En Endpoints compatible Instruct Mistral Quantized Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Asit03/mixtral-v0.3-full-16bit

Mixtral V0.3 Full 16bit Parameters and Internals

LLM Name	Mixtral V0.3 Full 16bit
Repository 🤗	https://huggingface.co/Asit03/mixtral-v0.3-full-16bit
Base Model(s)	unsloth/mistral-7b-instruct-v0.3-bnb-4bit unsloth/mistral-7b-instruct-v0.3-bnb-4bit
Model Size	7b
Required VRAM	14.5 GB
Updated	2026-09-08
Maintainer	Asit03
Model Type	mistral
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Supported Languages	en
Quantization Type	4bit
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.3
Tokenizer Class	LlamaTokenizerFast
Padding Token	[control_768]
Vocabulary Size	32768
Torch Data Type	bfloat16

Rank the Mixtral V0.3 Full 16bit capabilities

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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 Mixtral V0.3 Full 16bit

Model	Likes	Downloads	VRAM
Mixtral V0.3 Full 16bit V2	0	6	14 GB

Best Alternatives to Mixtral V0.3 Full 16bit

Best Alternatives	Context / RAM	Downloads	Likes
...aude Sonnet Mistral 7B It V0.3	32K / 14.5 GB	7	2
Mistral 7B Instruct V0.2 Fp16	32K / 14.4 GB	26	0
Mistral 7B Instruct V0.2 4bit	32K / 4.3 GB	79	1
...tral 7B Instruct V0.3 Bnb 4bit	32K / 4.1 GB	34633	42
Mistral 7B Instruct V0.2 8bit	32K / 7.6 GB	5	1
...tral 7B Instruct V0.2 Bnb 4bit	32K / 4.1 GB	7160	35
... 7B Instruct V0.3ContinuedFine	32K / 14.5 GB	14	0
Mistral 7B Instruct V0.3 4bit	32K / 4.1 GB	12492	10
...eMistral 7B Instruct Ead 16bit	32K / 14.5 GB	38	0
NaturalLM 7B Instruct	32K / 14.5 GB	17	10

Note: green Score (e.g. "73.2") means that the model is better than Asit03/mixtral-v0.3-full-16bit.