What are the hardware requirements for NebulaNet V2 4x7B MoE?

NebulaNet V2 4x7B MoE requires approximately 8.8 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 NebulaNet V2 4x7B MoE and how large is it?

NebulaNet V2 4x7B MoE is developed by davideuler. 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 NebulaNet V2 4x7B MoE?

NebulaNet V2 4x7B MoE is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

NebulaNet V2 4x7B MoE by davideuler — VRAM 8.8GB, 32K context

Name: NebulaNet V2 4x7B MoE
Author: davideuler

NebulaNet V2 4x7B MoE is an open-source language model by davideuler. Features: LLM, VRAM: 8.8GB, Context: 32K, License: mit, MoE, Quantized, LLM Explorer Score: 0.11.

Base model:contextualai/contex... Base model:quantized:contextua... Coding Conversational Endpoints compatible Gguf Mixtral Moe Multilingual Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/davideuler/NebulaNet-v2-4x7B-moe

NebulaNet V2 4x7B MoE Parameters and Internals

Model Type

text generation

Use Cases

Areas:

coding, multi language translation, chat, math

Additional Notes

NebulaNet-v2 is an MOE (Mixture of Experts) model consisting of 4 expert models, each with 7 billion parameters.

Supported Languages

multilingual ()

LLM Name	NebulaNet V2 4x7B MoE
Repository 🤗	https://huggingface.co/davideuler/NebulaNet-v2-4x7B-moe
Base Model(s)	Contextual KTO Mistral PairRM ContextualAI/Contextual_KTO_Mistral_PairRM
Required VRAM	8.8 GB
Updated	2026-07-30
Maintainer	davideuler
Model Type	mixtral
Model Files	8.8 GB 19.8 GB 25.7 GB
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	MixtralForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.1
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the NebulaNet V2 4x7B MoE 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 NebulaNet V2 4x7B MoE

Best Alternatives	Context / RAM	Downloads	Likes
WizardLM2 2bit	64K / 4.8 GB	155	0
Dolphin 2.7 Mixtral 8x7b GGUF	32K / 15.6 GB	1162	5
Cerebrum 1.0 8x7b GGUF	32K / 17.3 GB	18	1
...Hermes 2 Mixtral 8x7B SFT GGUF	32K / 17.3 GB	788	4
...ixtral 8x7B Instruct V0.1 GGUF	32K / 17.3 GB	507	5
...Hermes 2 Mixtral 8x7B DPO GGUF	32K / 17.3 GB	277	2
...oE V0.1 DPO F16 4.0bpw H6 EXL2	195K / 31.3 GB	7	0
...oE V0.1 DPO F16 5.0bpw H6 EXL2	195K / 38.8 GB	6	0
...2 Mixtral 8x22b 6.0bpw H8 EXL2	64K / 105.8 GB	2	1
WizardLM 2 8x22 EXL2 4.0bpw	64K / 70.9 GB	7	1

Note: green Score (e.g. "73.2") means that the model is better than davideuler/NebulaNet-v2-4x7B-moe.