What are the hardware requirements for 30B Epsilon?

30B Epsilon requires approximately 65.2 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed 30B Epsilon and how large is it?

30B Epsilon is developed by CalderaAI, a model with 30b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does 30B Epsilon perform on standard benchmarks?

30B Epsilon has the following published scores: MMLU 56.89. Compare against reference models on this page or on the LLM Explorer leaderboards.

30B Epsilon by CalderaAI — VRAM 65.2GB, 2K context

Name: 30B Epsilon
Author: CalderaAI

30B Epsilon is an open-source language model by CalderaAI. Features: 30b LLM, VRAM: 65.2GB, Context: 2K, LLM Explorer Score: 0.1, Arc: 63.1, HellaSwag: 83.6, MMLU: 56.9, GSM8K: 24.6.

Model Card on HF 🤗: https://huggingface.co/CalderaAI/30B-Epsilon

30B Epsilon Parameters and Internals

Model Type

instruct, general purpose, uncensored

Use Cases

Areas:

text generation, adventure roleplay, storytelling

Primary Use Cases:

text based adventure game, creative storytelling

Additional Notes

Model assembled from handpicked models and LoRAs. Allows personalized instruction following through contextual memory input in specialized UIs.

Training Details

Methodology:

Experimental use of LoRAs on language models and model merges.

Input Output

Input Format:

Alpaca format

Accepted Modalities:

text

LLM Name	30B Epsilon
Repository 🤗	https://huggingface.co/CalderaAI/30B-Epsilon
Model Size	30b
Required VRAM	65.2 GB
Updated	2026-08-27
Maintainer	CalderaAI
Model Type	llama
Model Files	16.9 GB 9.8 GB: 1-of-7 10.0 GB: 2-of-7 9.9 GB: 3-of-7 9.9 GB: 4-of-7 9.9 GB: 5-of-7 10.0 GB: 6-of-7 5.7 GB: 7-of-7
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.28.1
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the 30B Epsilon 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

Quantized Models of the 30B Epsilon

Model	Likes	Downloads	VRAM
30B Epsilon GGUF	4	249	13 GB
30B Epsilon AWQ	4	9	17 GB
30B Epsilon GPTQ	5	74	16 GB

Best Alternatives to 30B Epsilon

Best Alternatives	Context / RAM	Downloads	Likes
TildeOpen 30B	64K / 60.9 GB	4576	165
TildeOpen 30B 64K	64K / 60.9 GB	702	3
Tildeopen 30B Mu Instruct	64K / 60.9 GB	41	6
Flash Llama 30M 20001	32K / 0.1 GB	134	0
Smaug Slerp 30B V0.1	32K / 60.4 GB	79	0
Tenebra 30B Alpha01	16K / 65 GB	50	16
Llama33b 16K	16K / 65.2 GB	1	1
Veyra2 Mango 30M Base	4K / 0.1 GB	0	5
Yayi2 30B Llama	4K / 121.2 GB	922	22
... Tokens By Perplexity Bottom K	4K / 5.4 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than CalderaAI/30B-Epsilon.