What are the hardware requirements for FusionNet 34Bx2 MoE V0.1?

FusionNet 34Bx2 MoE V0.1 requires approximately 121.2 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 FusionNet 34Bx2 MoE V0.1 and how large is it?

FusionNet 34Bx2 MoE V0.1 is developed by TomGrc, 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 FusionNet 34Bx2 MoE V0.1 perform on standard benchmarks?

FusionNet 34Bx2 MoE V0.1 has the following published scores: MMLU 76.72. Compare against reference models on this page or on the LLM Explorer leaderboards.

FusionNet 34Bx2 MoE V0.1 by TomGrc — VRAM 121.2GB, 195K context

Name: FusionNet 34Bx2 MoE V0.1
Author: TomGrc

FusionNet 34Bx2 MoE V0.1 is an open-source language model by TomGrc. Features: 60.8b LLM, VRAM: 121.2GB, Context: 195K, License: mit, MoE, LLM Explorer Score: 0.11, Arc: 73.7, HellaSwag: 86.5, MMLU: 76.7, GSM8K: 73.

Conversational En Endpoints compatible Mixtral Model-index Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE_v0.1

FusionNet 34Bx2 MoE V0.1 Parameters and Internals

LLM Name	FusionNet 34Bx2 MoE V0.1
Repository 🤗	https://huggingface.co/TomGrc/FusionNet_34Bx2_MoE_v0.1
Model Size	60.8b
Required VRAM	121.2 GB
Updated	2026-08-05
Maintainer	TomGrc
Model Type	mixtral
Model Files	3.9 GB: 1-of-32 3.8 GB: 2-of-32 3.8 GB: 3-of-32 3.8 GB: 4-of-32 3.8 GB: 5-of-32 3.8 GB: 6-of-32 3.8 GB: 7-of-32 3.8 GB: 8-of-32 3.8 GB: 9-of-32 3.8 GB: 10-of-32 3.8 GB: 11-of-32 3.8 GB: 12-of-32 3.8 GB: 13-of-32 3.8 GB: 14-of-32 3.8 GB: 15-of-32 3.8 GB: 16-of-32 3.8 GB: 17-of-32 3.8 GB: 18-of-32 3.8 GB: 19-of-32 3.8 GB: 20-of-32 3.8 GB: 21-of-32 3.8 GB: 22-of-32 3.8 GB: 23-of-32 3.8 GB: 24-of-32 3.8 GB: 25-of-32 3.8 GB: 26-of-32 3.8 GB: 27-of-32 3.8 GB: 28-of-32 4.0 GB: 29-of-32 4.0 GB: 30-of-32 3.9 GB: 31-of-32 2.8 GB: 32-of-32
Supported Languages	en
Model Architecture	MixtralForCausalLM
License	mit
Context Length	200000
Model Max Length	200000
Transformers Version	4.37.2
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	64000
Torch Data Type	bfloat16

Rank the FusionNet 34Bx2 MoE V0.1 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 V0.1

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
Yi 34Bx2 MoE 60B	195K / 121.9 GB	7954	64
...34Bx2 MoE V0.1 Full Linear DPO	195K / 121.8 GB	68	2
... 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 TomGrc/FusionNet_34Bx2_MoE_v0.1.