What are the hardware requirements for TinyLlama 1.1B Chat V1.0 X2 MoE?

TinyLlama 1.1B Chat V1.0 X2 MoE requires approximately 3.7 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed TinyLlama 1.1B Chat V1.0 X2 MoE and how large is it?

TinyLlama 1.1B Chat V1.0 X2 MoE is developed by AIChenKai, a model with 1.1b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does TinyLlama 1.1B Chat V1.0 X2 MoE perform on standard benchmarks?

TinyLlama 1.1B Chat V1.0 X2 MoE has the following published scores: MMLU 24.81. Compare against reference models on this page or on the LLM Explorer leaderboards.

TinyLlama 1.1B Chat V1.0 X2 MoE by AIChenKai — VRAM 3.7GB, 2K context

Name: TinyLlama 1.1B Chat V1.0 X2 MoE
Author: AIChenKai

TinyLlama 1.1B Chat V1.0 X2 MoE is an open-source language model by AIChenKai. Features: 1.1b LLM, VRAM: 3.7GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.16, Arc: 36, HellaSwag: 61, MMLU: 24.8, GSM8K: 2.3.

Conversational Endpoints compatible Mixtral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/AIChenKai/TinyLlama-1.1B-Chat-v1.0-x2-MoE

TinyLlama 1.1B Chat V1.0 X2 MoE Parameters and Internals

LLM Name	TinyLlama 1.1B Chat V1.0 X2 MoE
Repository 🤗	https://huggingface.co/AIChenKai/TinyLlama-1.1B-Chat-v1.0-x2-MoE
Model Size	1.1b
Required VRAM	3.7 GB
Updated	2026-09-03
Maintainer	AIChenKai
Model Type	mixtral
Model Files	3.7 GB: 1-of-1
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the TinyLlama 1.1B Chat V1.0 X2 MoE 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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