What are the hardware requirements for FusionNet 34Bx2 MoE GPTQ?

FusionNet 34Bx2 MoE GPTQ requires approximately 31.8 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed FusionNet 34Bx2 MoE GPTQ and how large is it?

FusionNet 34Bx2 MoE GPTQ is developed by TheBloke, 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.

Where can I download or evaluate FusionNet 34Bx2 MoE GPTQ?

FusionNet 34Bx2 MoE GPTQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

FusionNet 34Bx2 MoE GPTQ by TheBloke — VRAM 31.8GB, 32K context

Name: FusionNet 34Bx2 MoE GPTQ
Author: TheBloke

FusionNet 34Bx2 MoE GPTQ is an open-source language model by TheBloke. Features: 60.8b LLM, VRAM: 31.8GB, Context: 32K, License: mit, MoE, Quantized, LLM Explorer Score: 0.11.

4-bit Base model:quantized:tomgrc/fu... Base model:tomgrc/fusionnet 34... Conversational En Gptq Mixtral Moe Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GPTQ

FusionNet 34Bx2 MoE GPTQ Parameters and Internals

Model Type

mixtral

Supported Languages

en (fine-tuned)

Input Output

Input Format:

[INST] <> {system_message} <> {prompt} [/INST]

LLM Name	FusionNet 34Bx2 MoE GPTQ
Repository 🤗	https://huggingface.co/TheBloke/FusionNet_34Bx2_MoE-GPTQ
Model Name	FusionNet 34Bx2 MoE
Model Creator	Suqin Zhang
Base Model(s)	FusionNet 34Bx2 MoE TomGrc/FusionNet_34Bx2_MoE
Model Size	60.8b
Required VRAM	31.8 GB
Updated	2026-07-31
Maintainer	TheBloke
Model Type	mixtral
Model Files	31.8 GB
Supported Languages	en
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	MixtralForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	64000
Torch Data Type	bfloat16

Rank the FusionNet 34Bx2 MoE GPTQ 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 FusionNet 34Bx2 MoE GPTQ

Best Alternatives	Context / RAM	Downloads	Likes
Mixtral 34Bx2 MoE 60B GPTQ	195K / 31.8 GB	50	7
Bagel Hermes 2x34b GPTQ	195K / 31.8 GB	3	2
...sionNet 34Bx2 MoE V0.1 DPO F16	195K / 121.8 GB	8097	14
60B MoE Coder V3	195K / 121.8 GB	70	3
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
Yi 34Bx2 MoE 60B	195K / 121.9 GB	7954	64
...34Bx2 MoE V0.1 Full Linear DPO	195K / 121.8 GB	68	2

Note: green Score (e.g. "73.2") means that the model is better than TheBloke/FusionNet_34Bx2_MoE-GPTQ.