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

Mixtral 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 Mixtral 34Bx2 MoE 60B and how large is it?

Mixtral 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 Mixtral 34Bx2 MoE 60B perform on standard benchmarks?

Mixtral 34Bx2 MoE 60B has the following published scores: MMLU 77.28. Compare against reference models on this page or on the LLM Explorer leaderboards.

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

Name: Mixtral 34Bx2 MoE 60B
Rating: 4 (1 reviews)
Author: cloudyu

Mixtral 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: 0.22, Arc: 71.3, HellaSwag: 85.4, MMLU: 77.3, GSM8K: 74.6.

Base model:finetune:sustech/su... Base model:sustech/sus-chat-34... Endpoints compatible Mixtral Model-index Moe Region:us Safetensors Sharded Tensorflow Yi

Model Card on HF 🤗: https://huggingface.co/cloudyu/Mixtral_34Bx2_MoE_60B

Mixtral 34Bx2 MoE 60B Parameters and Internals

LLM Name	Mixtral 34Bx2 MoE 60B
Repository 🤗	https://huggingface.co/cloudyu/Mixtral_34Bx2_MoE_60B
Base Model(s)	jondurbin/bagel-dpo-34b-v0.2 SUS Chat 34B jondurbin/bagel-dpo-34b-v0.2 SUSTech/SUS-Chat-34B
Model Size	60.8b
Required VRAM	121.9 GB
Updated	2026-08-08
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 Mixtral 34Bx2 MoE 60B 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

Quantized Models of the Mixtral 34Bx2 MoE 60B

Model	Likes	Downloads	VRAM
Mixtral 34Bx2 MoE 60B GGUF	35	2230	20 GB
Mixtral 34Bx2 MoE 60B GPTQ	7	50	31 GB
Mixtral 34Bx2 MoE 60B AWQ	4	7	32 GB

Best Alternatives to Mixtral 34Bx2 MoE 60B

Best Alternatives	Context / RAM	Downloads	Likes
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
Yi 34Bx2 MoE 60B	195K / 121.9 GB	7954	64
...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