What are the hardware requirements for Affine 5EqYW8McUc Af10?

Affine 5EqYW8McUc Af10 requires approximately 70.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Affine 5EqYW8McUc Af10 and how large is it?

Affine 5EqYW8McUc Af10 is developed by Tok331102, a model with 35.1b 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 Affine 5EqYW8McUc Af10?

Affine 5EqYW8McUc Af10 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Affine 5EqYW8McUc Af10 by Tok331102 — VRAM 70.2GB

Name: Affine 5EqYW8McUc Af10
Author: Tok331102

Affine 5EqYW8McUc Af10 is an open-source language model by Tok331102. Features: 35.1b LLM, VRAM: 70.2GB, LLM Explorer Score: 0.31.

Qwen3 5 moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Tok331102/affine-5EqYW8McUc-af10

Affine 5EqYW8McUc Af10 Parameters and Internals

LLM Name	Affine 5EqYW8McUc Af10
Repository 🤗	https://huggingface.co/Tok331102/affine-5EqYW8McUc-af10
Model Size	35.1b
Required VRAM	70.2 GB
Updated	2026-08-10
Maintainer	Tok331102
Model Type	qwen3_5_moe
Model Files	35.1 GB: 1-of-2 35.1 GB: 2-of-2
Model Architecture	Qwen3_5MoeForConditionalGeneration
Model Max Length	262144
Transformers Version	5.14.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Affine 5EqYW8McUc Af10 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 Affine 5EqYW8McUc Af10

Best Alternatives	Context / RAM	Downloads	Likes
Nex N2 Mini	0K / 70.4 GB	2289	294
Agents A1 Bf16	0K / 69.9 GB	596	3
GRM 3.2 Sky MLX BF16	0K / 69.9 GB	15	0

Note: green Score (e.g. "73.2") means that the model is better than Tok331102/affine-5EqYW8McUc-af10.