What are the hardware requirements for Opus V1.2 70B AWQ?

Opus V1.2 70B AWQ requires approximately 36.7 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 Opus V1.2 70B AWQ and how large is it?

Opus V1.2 70B AWQ is developed by dreamgen, 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 Opus V1.2 70B AWQ?

Opus V1.2 70B AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Opus V1.2 70B AWQ by dreamgen — VRAM 36.7GB, 32K context

Name: Opus V1.2 70B AWQ
Author: dreamgen

Opus V1.2 70B AWQ is an open-source language model by dreamgen. Features: 70b LLM, VRAM: 36.7GB, Context: 32K, Quantized, LLM Explorer Score: 0.11.

4-bit Awq Axolotl Conversational En Endpoints compatible Llama Quantized Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/dreamgen/opus-v1.2-70b-awq

Opus V1.2 70B AWQ Parameters and Internals

Model Type

text generation

Additional Notes

The model is fine-tuned for steerable story-writing and role-playing.

LLM Name	Opus V1.2 70B AWQ
Repository 🤗	https://huggingface.co/dreamgen/opus-v1.2-70b-awq
Base Model(s)	dreamgen/opus-v1.2-70b dreamgen/opus-v1.2-70b
Model Size	70b
Required VRAM	36.7 GB
Updated	2026-08-05
Maintainer	dreamgen
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
Supported Languages	en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	float16

Rank the Opus V1.2 70B AWQ 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 Opus V1.2 70B AWQ

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
QuartetAnemoi 70B T0.0001 AWQ	31K / 36.7 GB	3	1
Senku 70B AWQ 4bit GEMM	31K / 36.7 GB	2	1
Kiqu 70B AWQ	31K / 36.7 GB	1	1

Note: green Score (e.g. "73.2") means that the model is better than dreamgen/opus-v1.2-70b-awq.