What are the hardware requirements for ML2 123B Magnum Diamond?

ML2 123B Magnum Diamond requires approximately 212.4 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed ML2 123B Magnum Diamond and how large is it?

ML2 123B Magnum Diamond is developed by Doctor-Shotgun, a model with 123b 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 ML2 123B Magnum Diamond?

ML2 123B Magnum Diamond is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

ML2 123B Magnum Diamond by Doctor-Shotgun — VRAM 212.4GB, 128K context

Name: ML2 123B Magnum Diamond
Author: Doctor-Shotgun

ML2 123B Magnum Diamond is an open-source language model by Doctor-Shotgun. Features: 123b LLM, VRAM: 212.4GB, Context: 128K, License: other, Instruction-Based, LLM Explorer Score: 0.19.

Axolotl Base model:finetune:mistralai/... Base model:mistralai/mistral-l... Chat Conversational En Endpoints compatible Instruct Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Doctor-Shotgun/ML2-123B-Magnum-Diamond

ML2 123B Magnum Diamond Parameters and Internals

LLM Name	ML2 123B Magnum Diamond
Repository 🤗	https://huggingface.co/Doctor-Shotgun/ML2-123B-Magnum-Diamond
Base Model(s)	Mistral Large Instruct 2411 mistralai/Mistral-Large-Instruct-2411
Model Size	123b
Required VRAM	212.4 GB
Updated	2026-08-12
Maintainer	Doctor-Shotgun
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-51 4.8 GB: 2-of-51 4.9 GB: 3-of-51 4.8 GB: 4-of-51 4.8 GB: 5-of-51 4.8 GB: 6-of-51 4.9 GB: 7-of-51 4.8 GB: 8-of-51 4.8 GB: 9-of-51 4.8 GB: 10-of-51 4.9 GB: 11-of-51 4.8 GB: 12-of-51 4.8 GB: 13-of-51 4.8 GB: 14-of-51 4.9 GB: 15-of-51 4.8 GB: 16-of-51 4.8 GB: 17-of-51 4.8 GB: 18-of-51 4.9 GB: 19-of-51 4.8 GB: 20-of-51 4.8 GB: 21-of-51 4.8 GB: 22-of-51 4.9 GB: 23-of-51 4.8 GB: 24-of-51 4.8 GB: 25-of-51 4.8 GB: 26-of-51 4.9 GB: 27-of-51 4.8 GB: 28-of-51 4.8 GB: 29-of-51 4.8 GB: 30-of-51 4.9 GB: 31-of-51 4.8 GB: 32-of-51 4.8 GB: 33-of-51 4.8 GB: 34-of-51 4.9 GB: 35-of-51 4.8 GB: 36-of-51 4.8 GB: 37-of-51 4.8 GB: 38-of-51 4.9 GB: 39-of-51 4.8 GB: 40-of-51 4.8 GB: 41-of-51 4.8 GB: 42-of-51 4.9 GB: 43-of-51 4.8 GB: 44-of-51
Supported Languages	en
Model Architecture	MistralForCausalLM
License	other
Context Length	131072
Model Max Length	131072
Transformers Version	4.52.4
Vocabulary Size	32768
Torch Data Type	bfloat16

Rank the ML2 123B Magnum Diamond 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 ML2 123B Magnum Diamond

Best Alternatives	Context / RAM	Downloads	Likes
Behemoth ReduX 123B V1	128K / 217.2 GB	70	14
Behemoth X 123B V2	128K / 217.2 GB	47	32
Behemoth R1 123B V2	128K / 226.4 GB	12	29
Gigaberg Mistral Large 123B	128K / 222 GB	6	2
Cakrawala 123B	128K / 222 GB	6	4
Behemoth 123B V2	128K / 221.6 GB	4	10
Behemoth 123B V1	128K / 221.6 GB	6	44
Magnum V4 123B	128K / 222 GB	22	33
Magnum V2 123B	128K / 207.6 GB	62	64
Lumikabra 123B V0.4	128K / 216.7 GB	9	12

Note: green Score (e.g. "73.2") means that the model is better than Doctor-Shotgun/ML2-123B-Magnum-Diamond.