What are the hardware requirements for Yi 34Bx2 MoE 60B?

Yi 34Bx2 MoE 60B requires approximately 121.9 GB of VRAM and supports a context window of 195K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Yi 34Bx2 MoE 60B and how large is it?

Yi 34Bx2 MoE 60B is developed by cloudyu, a model with 60.8b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Yi 34Bx2 MoE 60B perform on standard benchmarks?

Yi 34Bx2 MoE 60B has the following published scores: MMLU 77.47. Compare against reference models on this page or on the LLM Explorer leaderboards.

Yi 34Bx2 MoE 60B by cloudyu — VRAM 121.9GB, 195K context

Name: Yi 34Bx2 MoE 60B
Author: cloudyu

Yi 34Bx2 MoE 60B is an open-source language model by cloudyu. Features: 60.8b LLM, VRAM: 121.9GB, Context: 195K, License: apache-2.0, MoE, LLM Explorer Score: 0.14, Arc: 71.1, HellaSwag: 85.2, MMLU: 77.5, GSM8K: 75.5.

Conversational Endpoints compatible Mixtral Moe Region:us Safetensors Sharded Tensorflow Yi

Model Card on HF 🤗: https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B

Yi 34Bx2 MoE 60B Parameters and Internals

Model Type

MoE, Causal LM

Additional Notes

This model is a variation of the cloudyu/Mixtral_34Bx2_MoE_60B focusing on English and Chinese language capabilities.

Supported Languages

English (Proficient), Chinese (Proficient)

Input Output

Input Format:

text input

Accepted Modalities:

text

Output Format:

text output

Performance Tips:

Use GPUs for better performance, with configurations available for both GPU and CPU.

LLM Name	Yi 34Bx2 MoE 60B
Repository 🤗	https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B
Model Size	60.8b
Required VRAM	121.9 GB
Updated	2026-07-06
Maintainer	cloudyu
Model Type	mixtral
Model Files	9.8 GB: 1-of-13 10.0 GB: 2-of-13 10.0 GB: 3-of-13 10.0 GB: 4-of-13 10.0 GB: 5-of-13 10.0 GB: 6-of-13 10.0 GB: 7-of-13 10.0 GB: 8-of-13 10.0 GB: 9-of-13 10.0 GB: 10-of-13 10.0 GB: 11-of-13 10.0 GB: 12-of-13 2.1 GB: 13-of-13
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	200000
Model Max Length	200000
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	64000
Torch Data Type	bfloat16

Rank the Yi 34Bx2 MoE 60B 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

Quantized Models of the Yi 34Bx2 MoE 60B

Model	Likes	Downloads	VRAM
Yi 34Bx2 MoE 60B GGUF	2	308	0 GB

Best Alternatives to Yi 34Bx2 MoE 60B

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 34Bx2 MoE 60B	195K / 121.9 GB	7876	114
Yi 34Bx2 MoE 60B DPO	195K / 121.8 GB	8035	3
Bagel Hermes 2x34B	195K / 121.9 GB	70	16
Yi 34Bx2 MoE 200K	195K / 121.9 GB	7897	2
...34Bx2 MoE V0.1 Full Linear DPO	195K / 121.8 GB	68	2
FusionNet 34Bx2 MoE V0.1	195K / 121.2 GB	52	8
... Cloudyu Mixtral 34Bx2 MoE 60B	195K / 121.8 GB	57	0
...DPO TomGrc FusionNet 34Bx2 MoE	32K / 121.8 GB	81	4
FusionNet 34Bx2 MoE	32K / 121.2 GB	65	9
Nous Hermes 2 MoE 2x34B	4K / 121.9 GB	96	0

Note: green Score (e.g. "73.2") means that the model is better than cloudyu/Yi-34Bx2-MoE-60B.