What are the hardware requirements for WhiteRabbitNeo 33B V1?

WhiteRabbitNeo 33B V1 requires approximately 67 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed WhiteRabbitNeo 33B V1 and how large is it?

WhiteRabbitNeo 33B V1 is developed by WhiteRabbitNeo, a model with 33b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does WhiteRabbitNeo 33B V1 perform on standard benchmarks?

WhiteRabbitNeo 33B V1 has the following published scores: MMLU 40.56. Compare against reference models on this page or on the LLM Explorer leaderboards.

WhiteRabbitNeo 33B V1 by WhiteRabbitNeo — VRAM 67GB, 16K context

Name: WhiteRabbitNeo 33B V1
Author: WhiteRabbitNeo

WhiteRabbitNeo 33B V1 is an open-source language model by WhiteRabbitNeo. Features: 33b LLM, VRAM: 67GB, Context: 16K, License: other, Code Generating, LLM Explorer Score: 0.14, Arc: 44.4, HellaSwag: 60.2, MMLU: 40.6, GSM8K: 33.7.

Codegen Deploy:azure Endpoints compatible Llama Pytorch Region:us Sharded

Model Card on HF 🤗: https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-33B-v1

WhiteRabbitNeo 33B V1 Parameters and Internals

Model Type

cybersecurity, text generation

Use Cases

Areas:

cybersecurity

Applications:

offensive cybersecurity, defensive cybersecurity

Additional Notes

The model includes a "Prompt Enhancement" feature for improved performance. It is used both in offensive and defensive cybersecurity contexts and pilots a public preview to assess its societal impact. The model uses a cognitive reasoning process it terms "Tree of Thoughts" to provide comprehensive answers.

LLM Name	WhiteRabbitNeo 33B V1
Repository 🤗	https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-33B-v1
Model Size	33b
Required VRAM	67 GB
Updated	2026-08-01
Maintainer	WhiteRabbitNeo
Model Type	llama
Model Files	4.9 GB: 1-of-14 4.8 GB: 2-of-14 4.8 GB: 3-of-14 4.8 GB: 4-of-14 4.8 GB: 5-of-14 4.8 GB: 6-of-14 4.8 GB: 7-of-14 4.8 GB: 8-of-14 4.8 GB: 9-of-14 4.8 GB: 10-of-14 4.8 GB: 11-of-14 4.8 GB: 12-of-14 4.8 GB: 13-of-14 4.5 GB: 14-of-14
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	other
Context Length	16384
Model Max Length	16384
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Padding Token	<｜end▁of▁sentence｜>
Vocabulary Size	32256
Torch Data Type	bfloat16

Rank the WhiteRabbitNeo 33B V1 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

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ReflectionCoder DS 33B	16K / 67 GB	8293	4
Deepseek Wizard 33B Slerp	16K / 35.3 GB	7	0
ValidateAI 3 33B Ties	16K / 66.5 GB	7	0
ValidateAI 2 33B AT	16K / 66.5 GB	6	0
Everyone Coder 33B Base	16K / 66.5 GB	86	21
Deepseek Coder 33B Instruct	16K / 66.5 GB	3376	580
Fortran2Cpp	16K / 67.3 GB	11	4
F2C Translator	16K / 67.3 GB	10	1
Llm4decompile 33B	16K / 66.5 GB	15	8
Deepseek Coder 33B Base	16K / 66.5 GB	2052	76

Note: green Score (e.g. "73.2") means that the model is better than WhiteRabbitNeo/WhiteRabbitNeo-33B-v1.