What are the hardware requirements for Sage Ft Mixtral 8x7b?

Sage Ft Mixtral 8x7b requires approximately 90 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 Sage Ft Mixtral 8x7b and how large is it?

Sage Ft Mixtral 8x7b is developed by apple, a model with 46.7b 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 Sage Ft Mixtral 8x7b?

Sage Ft Mixtral 8x7b is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Sage Ft Mixtral 8x7b by apple — VRAM 90GB, 32K context

Name: Sage Ft Mixtral 8x7b
Author: apple

Sage Ft Mixtral 8x7b is an open-source language model by apple. Features: 46.7b LLM, VRAM: 90GB, Context: 32K, License: apache-2.0, MoE, Instruction-Based, LLM Explorer Score: 0.17.

Arxiv:2503.03040 Base model:finetune:mistralai/... Base model:mistralai/mixtral-8... Conversational Conversational-ai Dataset:sharegpt Dialog-generation En Endpoints compatible Instruct Mixtral Moe Region:us Safetensors Sharded State-action-model Tensorflow

Model Card on HF 🤗: https://huggingface.co/apple/sage-ft-mixtral-8x7b

Sage Ft Mixtral 8x7b Parameters and Internals

LLM Name	Sage Ft Mixtral 8x7b
Repository 🤗	https://huggingface.co/apple/sage-ft-mixtral-8x7b
Base Model(s)	mistralai/Mixtral-8x7B-Instruct-v0.1 mistralai/Mixtral-8x7B-Instruct-v0.1
Model Size	46.7b
Required VRAM	90 GB
Updated	2026-08-01
Maintainer	apple
Model Type	mixtral
Instruction-Based	Yes
Model Files	2.0 GB: 1-of-48 2.0 GB: 2-of-48 2.0 GB: 3-of-48 2.0 GB: 4-of-48 2.0 GB: 5-of-48 2.0 GB: 6-of-48 2.0 GB: 7-of-48 2.0 GB: 8-of-48 2.0 GB: 9-of-48 2.0 GB: 10-of-48 2.0 GB: 11-of-48 2.0 GB: 12-of-48 2.0 GB: 13-of-48 2.0 GB: 14-of-48 2.0 GB: 15-of-48 2.0 GB: 16-of-48 2.0 GB: 17-of-48 2.0 GB: 18-of-48 2.0 GB: 19-of-48 2.0 GB: 20-of-48 2.0 GB: 21-of-48 2.0 GB: 22-of-48 2.0 GB: 23-of-48 2.0 GB: 24-of-48 2.0 GB: 25-of-48 2.0 GB: 26-of-48 2.0 GB: 27-of-48 2.0 GB: 28-of-48 2.0 GB: 29-of-48 2.0 GB: 30-of-48 2.0 GB: 31-of-48 2.0 GB: 32-of-48 2.0 GB: 33-of-48 2.0 GB: 34-of-48 2.0 GB: 35-of-48 2.0 GB: 36-of-48 2.0 GB: 37-of-48 2.0 GB: 38-of-48 2.0 GB: 39-of-48 2.0 GB: 40-of-48 2.0 GB: 41-of-48 2.0 GB: 42-of-48 2.0 GB: 43-of-48 2.0 GB: 44-of-48 2.0 GB: 45-of-48
Supported Languages	en
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.3
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Sage Ft Mixtral 8x7b 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 Sage Ft Mixtral 8x7b

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	842893	4720
Mixtral 8x7B Instruct V0.1	32K / 93.6 GB	7	0
Mixtral 8x7B Instruct V0.1 FP8	32K / 47.1 GB	3383	0
Merge Mixtral Prometheus 8x7B	32K / 91.9 GB	12	2
...xtral 8x7B Yes Instruct LimaRP	32K / 93.5 GB	5	1
Notux 8x7b V1	32K / 93.6 GB	43	165
BagelMIsteryTour V2 8x7B	32K / 93.5 GB	94	17
...rkrautLM Mixtral 8x7B Instruct	32K / 93.6 GB	84	22
Mixtral 8x7B Instruct V0.1 FP8	32K / 47.1 GB	374	0
Dolphin 2.5 Mixtral 8x7b	32K / 93.6 GB	4125	1235

Note: green Score (e.g. "73.2") means that the model is better than apple/sage-ft-mixtral-8x7b.