What are the hardware requirements for Dolphin 2.9.1 Yi 1.5 34B AWQ?

Dolphin 2.9.1 Yi 1.5 34B AWQ requires approximately 19.2 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Dolphin 2.9.1 Yi 1.5 34B AWQ and how large is it?

Dolphin 2.9.1 Yi 1.5 34B AWQ is developed by shunran, a model with 34b 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 Dolphin 2.9.1 Yi 1.5 34B AWQ?

Dolphin 2.9.1 Yi 1.5 34B AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Dolphin 2.9.1 Yi 1.5 34B AWQ by shunran — VRAM 19.2GB, 8K context

Name: Dolphin 2.9.1 Yi 1.5 34B AWQ
Author: shunran

Dolphin 2.9.1 Yi 1.5 34B AWQ is an open-source language model by shunran. Features: 34b LLM, VRAM: 19.2GB, Context: 8K, Quantized, LLM Explorer Score: 0.12.

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

Model Card on HF 🤗: https://huggingface.co/shunran/dolphin-2.9.1-yi-1.5-34b-awq

Dolphin 2.9.1 Yi 1.5 34B AWQ Parameters and Internals

LLM Name	Dolphin 2.9.1 Yi 1.5 34B AWQ
Repository 🤗	https://huggingface.co/shunran/dolphin-2.9.1-yi-1.5-34b-awq
Base Model(s)	cognitivecomputations/dolphin-2.9.1-yi-1.5-34b cognitivecomputations/dolphin-2.9.1-yi-1.5-34b
Model Size	34b
Required VRAM	19.2 GB
Updated	2026-07-17
Maintainer	shunran
Model Type	llama
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 4.3 GB: 4-of-4
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.38.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	float16

Rank the Dolphin 2.9.1 Yi 1.5 34B AWQ 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

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