What are the hardware requirements for Senku 70B AWQ 4bit GEMM?

Senku 70B AWQ 4bit GEMM requires approximately 36.7 GB of VRAM and supports a context window of 31K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Senku 70B AWQ 4bit GEMM and how large is it?

Senku 70B AWQ 4bit GEMM is developed by bigquant, a model with 70b 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 Senku 70B AWQ 4bit GEMM?

Senku 70B AWQ 4bit GEMM is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Senku 70B AWQ 4bit GEMM by bigquant — VRAM 36.7GB, 31K context

Name: Senku 70B AWQ 4bit GEMM
Author: bigquant

Senku 70B AWQ 4bit GEMM is an open-source language model by bigquant. Features: 70b LLM, VRAM: 36.7GB, Context: 31K, Quantized, LLM Explorer Score: 0.11.

4-bit 4bit Awq Conversational Endpoints compatible Llama Miqu Quantized Region:us Safetensors Senku Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/bigquant/Senku-70B-AWQ-4bit-GEMM

Senku 70B AWQ 4bit GEMM Parameters and Internals

Model Type

quantized

Additional Notes

AWQ Quantization with settings: zero_point: True, q_group_size: 128, w_bit: 4, version: GEMM.

LLM Name	Senku 70B AWQ 4bit GEMM
Repository 🤗	https://huggingface.co/bigquant/Senku-70B-AWQ-4bit-GEMM
Model Size	70b
Required VRAM	36.7 GB
Updated	2026-07-22
Maintainer	bigquant
Model Type	llama
Model Files	5.0 GB: 1-of-8 5.0 GB: 2-of-8 4.9 GB: 3-of-8 4.9 GB: 4-of-8 4.9 GB: 5-of-8 4.9 GB: 6-of-8 4.9 GB: 7-of-8 2.2 GB: 8-of-8
AWQ Quantization	Yes
Quantization Type	awq\|4bit
Model Architecture	LlamaForCausalLM
Context Length	32764
Model Max Length	32764
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Senku 70B AWQ 4bit GEMM 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 Senku 70B AWQ 4bit GEMM

Best Alternatives	Context / RAM	Downloads	Likes
...0B Instruct Gradient 1048K AWQ	1024K / 39.9 GB	5	1
...70B Instruct Gradient 262K AWQ	256K / 39.9 GB	5	0
Llama 3.3 70B Instruct AWQ	128K / 39.9 GB	262002	46
...lama 3.3 70B Instruct AWQ INT4	128K / 39.9 GB	82744	30
Llama 3.3 70B Instruct AWQ	128K / 39.9 GB	16090	12
... SauerkrautLM 70B Instruct AWQ	128K / 39.9 GB	5	5
MultiVerse 70B AWQ	32K / 41.3 GB	70	2
Opus V1.2 70B AWQ	32K / 36.7 GB	9	1
QuartetAnemoi 70B T0.0001 AWQ	31K / 36.7 GB	3	1
Kiqu 70B AWQ	31K / 36.7 GB	1	1

Note: green Score (e.g. "73.2") means that the model is better than bigquant/Senku-70B-AWQ-4bit-GEMM.