What are the hardware requirements for Llama 3 Chatty 2x8B AWQ?

Llama 3 Chatty 2x8B AWQ requires approximately 8.7 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 Llama 3 Chatty 2x8B AWQ and how large is it?

Llama 3 Chatty 2x8B AWQ is developed by solidrust, a model with 13.7b 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 Llama 3 Chatty 2x8B AWQ?

Llama 3 Chatty 2x8B AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3 Chatty 2x8B AWQ by solidrust — VRAM 8.7GB, 8K context

Name: Llama 3 Chatty 2x8B AWQ
Author: solidrust

Llama 3 Chatty 2x8B AWQ is an open-source language model by solidrust. Features: 13.7b LLM, VRAM: 8.7GB, Context: 8K, MoE, Quantized, LLM Explorer Score: 0.12.

4-bit Autotrain compatible Awq Base model:quantized:undi95/ll... Base model:undi95/llama-3-chat... Conversational Endpoints compatible Mixtral Moe Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/solidrust/Llama-3-Chatty-2x8B-AWQ

Llama 3 Chatty 2x8B AWQ Parameters and Internals

Model Type

text-generation

Additional Notes

AWQ is an efficient, accurate, and fast low-bit weight quantization method that supports 4-bit quantization. It offers faster Transformers-based inference with equivalent or better quality compared to GPTQ. AWQ models are supported on Linux and Windows with NVidia GPUs. For macOS, use GGUF models.

LLM Name	Llama 3 Chatty 2x8B AWQ
Repository 🤗	https://huggingface.co/solidrust/Llama-3-Chatty-2x8B-AWQ
Base Model(s)	Llama 3 Chatty 2x8B Undi95/Llama-3-Chatty-2x8B
Model Size	13.7b
Required VRAM	8.7 GB
Updated	2026-05-26
Maintainer	solidrust
Model Type	mixtral
Model Files	5.0 GB: 1-of-2 3.7 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MixtralForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.41.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|begin_of_text\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Llama 3 Chatty 2x8B 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 Llama 3 Chatty 2x8B AWQ

Best Alternatives	Context / RAM	Downloads	Likes
L3.1 MoE 2x8B V0.2	128K / 27.3 GB	9	9
... Deepseek DeepHermes E32 13.7B	128K / 54.9 GB	5	0
HAI SER	128K / 27.3 GB	7	16
L3.1 Celestial Stone 2x8B	128K / 27.3 GB	312	3
...ma 3 2x8B Instruct MoE 64K Ctx	64K / 27.3 GB	12	4
Penny Llama3 2x8b	8K / 27.3 GB	6	1
Kilo 2x8B	8K / 27.5 GB	12	1
Defne Llama3 2x8B	8K / 27.4 GB	15	6
Llama 3 Elyza Youko MoE 2x8B	8K / 27.3 GB	10	0
Llama 3 ELYZA Hermes 2x8B	8K / 27.4 GB	3	1

Note: green Score (e.g. "73.2") means that the model is better than solidrust/Llama-3-Chatty-2x8B-AWQ.