What are the hardware requirements for Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx?

Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx requires approximately 73.5 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx and how large is it?

Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx is developed by nightmedia, a model with 80b 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 Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx?

Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx by nightmedia — VRAM 73.5GB, 256K context

Name: Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx
Author: nightmedia

Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx is an open-source language model by nightmedia. Features: 80b LLM, VRAM: 73.5GB, Context: 256K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.2.

8-bit Base model:quantized:unsloth/q... Base model:unsloth/qwen3-next-... Conversational Instruct Mlx Qwen3 next Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/nightmedia/unsloth-Qwen3-Next-80B-A3B-Instruct-qx86-hi-mlx

Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx Parameters and Internals

LLM Name	Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx
Repository 🤗	https://huggingface.co/nightmedia/unsloth-Qwen3-Next-80B-A3B-Instruct-qx86-hi-mlx
Base Model(s)	Qwen3 Next 80B A3B Instruct unsloth/Qwen3-Next-80B-A3B-Instruct
Model Size	80b
Required VRAM	73.5 GB
Updated	2025-09-23
Maintainer	nightmedia
Model Type	qwen3_next
Instruction-Based	Yes
Model Files	5.3 GB: 1-of-15 5.2 GB: 2-of-15 5.4 GB: 3-of-15 5.0 GB: 4-of-15 5.4 GB: 5-of-15 5.0 GB: 6-of-15 4.9 GB: 7-of-15 5.1 GB: 8-of-15 5.4 GB: 9-of-15 5.0 GB: 10-of-15 5.4 GB: 11-of-15 5.0 GB: 12-of-15 5.2 GB: 13-of-15 5.2 GB: 14-of-15 1.0 GB: 15-of-15
Model Architecture	Qwen3NextForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	4.57.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Errors	replace

Rank the Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx 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 Unsloth Qwen3 Next 80B A3B Instruct Qx86 Hi Mlx

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3 Next 80B A3B Instruct	256K / 162.7 GB	422	89
Qwen3 Next 80B A3B Instruct	256K / 162.7 GB	286679	1042
...wen3 Next 80B A3B Instruct FP8	256K / 81.8 GB	329583	89
Qwen3 Next MoE	256K / 0 GB	127090	4
...n3 Next 80B A3B Instruct NVFP4	256K / 50.7 GB	13898	39
...t 80B A3B Instruct FP8 Dynamic	256K / 80.5 GB	506	4
...ext 80B A3B Instruct Mxfp4 Mlx	256K / 42 GB	243	8
... Instruct Int4 Mixed AutoRound	256K / 43.3 GB	97	24
...0B A3B Instruct Int4 AutoRound	256K / 42.5 GB	120	10
...en3 Next 80B A3B Instruct 8bit	256K / 83.9 GB	4443	8

Note: green Score (e.g. "73.2") means that the model is better than nightmedia/unsloth-Qwen3-Next-80B-A3B-Instruct-qx86-hi-mlx.