What are the hardware requirements for Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples?

Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples requires approximately 32.1 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples?

Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples by jayzou3773 — VRAM 32.1GB, 40K context

Name: Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples
Author: jayzou3773

Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples is an open-source language model by jayzou3773. Features: 16b LLM, VRAM: 32.1GB, Context: 40K, MoE, LLM Explorer Score: 0.23.

Moe Qwen3 moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/jayzou3773/qwen3-moe-expert_drop-weight_magnitude_pruning-r64-s1k-128samples

Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples Parameters and Internals

LLM Name	Qwen3 MoE Expert Drop Weight Magnitude Pruning R64 S1k 128samples
Repository 🤗	https://huggingface.co/jayzou3773/qwen3-moe-expert_drop-weight_magnitude_pruning-r64-s1k-128samples
Model Size	16b
Required VRAM	32.1 GB
Updated	2026-05-30
Maintainer	jayzou3773
Model Type	qwen3_moe
Model Files	5.0 GB: 1-of-7 5.0 GB: 2-of-7 5.0 GB: 3-of-7 5.0 GB: 4-of-7 5.0 GB: 5-of-7 5.0 GB: 6-of-7 2.1 GB: 7-of-7
Model Architecture	Qwen3MoeForCausalLM
Context Length	40960
Model Max Length	40960
Transformers Version	4.53.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

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