What are the hardware requirements for Vlsi MoE Ffn Merged?

Vlsi MoE Ffn Merged requires approximately 65.5 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 Vlsi MoE Ffn Merged and how large is it?

Vlsi MoE Ffn Merged is developed by vxkyyy, a model with 32.8b 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 Vlsi MoE Ffn Merged?

Vlsi MoE Ffn Merged is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Vlsi MoE Ffn Merged by vxkyyy — VRAM 65.5GB, 32K context

Name: Vlsi MoE Ffn Merged
Author: vxkyyy

Vlsi MoE Ffn Merged is an open-source language model by vxkyyy. Features: 32.8b LLM, VRAM: 65.5GB, Context: 32K, MoE, LLM Explorer Score: 0.23.

Moe Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/vxkyyy/vlsi-moe-ffn-merged

Vlsi MoE Ffn Merged Parameters and Internals

LLM Name	Vlsi MoE Ffn Merged
Repository 🤗	https://huggingface.co/vxkyyy/vlsi-moe-ffn-merged
Model Size	32.8b
Required VRAM	65.5 GB
Updated	2026-08-08
Maintainer	vxkyyy
Model Type	qwen2
Model Files	49.9 GB: 1-of-2 15.6 GB: 2-of-2
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	5.8.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Errors	replace

Rank the Vlsi MoE Ffn Merged 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 Vlsi MoE Ffn Merged

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AM Thinking V1	128K / 65.7 GB	79	205
Magnum Hamanasu QwQ V2 R1	128K / 65.8 GB	12	0
Magnum Hamanasu QwQ V2 R1	128K / 65.8 GB	10	0
DRIVE RL	64K / 65.6 GB	42	12
QwQ Med 3	40K / 131.5 GB	23	2
INTELLECT 2	40K / 131.5 GB	75	204
FluentlyLM Prinum	32K / 65.8 GB	1436	31
PathFinderAi3.0	32K / 65.8 GB	4	1
...py Roleplay 1739875661 0711603	32K / 65.8 GB	54	0
...ppy Roleplay 1739875662 172876	32K / 65.8 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than vxkyyy/vlsi-moe-ffn-merged.