What are the hardware requirements for Mixtral 8x7b Instruct V0.1 Agent Function Calling?

Mixtral 8x7b Instruct V0.1 Agent Function Calling requires approximately 44.3 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.

Where can I download or evaluate Mixtral 8x7b Instruct V0.1 Agent Function Calling?

Mixtral 8x7b Instruct V0.1 Agent Function Calling is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mixtral 8x7b Instruct V0.1 Agent Function Calling by zember — VRAM 44.3GB, 32K context

Name: Mixtral 8x7b Instruct V0.1 Agent Function Calling
Author: zember

Mixtral 8x7b Instruct V0.1 Agent Function Calling is an open-source language model by zember. Features: LLM, VRAM: 44.3GB, Context: 32K, MoE, Instruction-Based, LLM Explorer Score: 0.11.

Conversational Endpoints compatible Instruct Mixtral Moe Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/zember/mixtral-8x7b-instruct-v0.1-agent-function-calling

Mixtral 8x7b Instruct V0.1 Agent Function Calling Parameters and Internals

LLM Name	Mixtral 8x7b Instruct V0.1 Agent Function Calling
Repository 🤗	https://huggingface.co/zember/mixtral-8x7b-instruct-v0.1-agent-function-calling
Required VRAM	44.3 GB
Updated	2026-08-08
Maintainer	zember
Model Type	mixtral
Instruction-Based	Yes
Model Files	0.9 GB: 1-of-98 0.9 GB: 2-of-98 0.9 GB: 3-of-98 0.9 GB: 4-of-98 0.9 GB: 5-of-98 0.9 GB: 6-of-98 0.9 GB: 7-of-98 0.9 GB: 8-of-98 0.9 GB: 9-of-98 0.9 GB: 10-of-98 0.9 GB: 11-of-98 0.9 GB: 12-of-98 0.9 GB: 13-of-98 0.9 GB: 14-of-98 0.9 GB: 15-of-98 0.9 GB: 16-of-98 0.9 GB: 17-of-98 0.9 GB: 18-of-98 1.0 GB: 19-of-98 1.0 GB: 20-of-98 0.9 GB: 21-of-98 1.0 GB: 22-of-98 1.0 GB: 23-of-98 0.9 GB: 24-of-98 1.0 GB: 25-of-98 1.0 GB: 26-of-98 0.9 GB: 27-of-98 1.0 GB: 28-of-98 1.0 GB: 29-of-98 0.9 GB: 30-of-98 1.0 GB: 31-of-98 1.0 GB: 32-of-98 0.9 GB: 33-of-98 1.0 GB: 34-of-98 1.0 GB: 35-of-98 0.9 GB: 36-of-98 1.0 GB: 37-of-98 1.0 GB: 38-of-98 0.9 GB: 39-of-98 1.0 GB: 40-of-98 1.0 GB: 41-of-98 0.9 GB: 42-of-98 1.0 GB: 43-of-98 1.0 GB: 44-of-98 0.9 GB: 45-of-98 1.0 GB: 46-of-98 1.0 GB: 47-of-98
Model Architecture	MixtralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.2
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mixtral 8x7b Instruct V0.1 Agent Function Calling 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

Best Alternatives to Mixtral 8x7b Instruct V0.1 Agent Function Calling

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...ixtral 8x22B Instruct V0.1 FP8	64K / 140.9 GB	98	0
Dolphin 2.6 Mixtral 8x7b	32K / 93.6 GB	11285	211
Dolphin 2.6 Mixtral 8x7b	32K / 93.6 GB	7806	217
...eqlen 4096 Bs 4 Optimum 0 0 23	32K / GB	8	0
...eqlen 4096 Bs 4 Optimum 0 0 23	32K / GB	9	1
Dolphin 2.7 Mixtral 8x7b	32K / 93.6 GB	2178	170
Empower Functions Medium	32K / 93.6 GB	17	1
Mixtral 8x7B Instruct V0.1	32K / GB	5	0
...ral 8x7b Instruct V0.1 Int4 Ov	32K / 0 GB	16	4
Dolphin 2.7 Mixtral 8x7b	32K / 93.6 GB	228	169

Note: green Score (e.g. "73.2") means that the model is better than zember/mixtral-8x7b-instruct-v0.1-agent-function-calling.