What are the hardware requirements for Mistral Helcyon Mercury 12B V3.2?

Mistral Helcyon Mercury 12B V3.2 requires approximately 24.5 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 Mistral Helcyon Mercury 12B V3.2 and how large is it?

Mistral Helcyon Mercury 12B V3.2 is developed by XeyonAI, a model with 12b 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 Mistral Helcyon Mercury 12B V3.2?

Mistral Helcyon Mercury 12B V3.2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mistral Helcyon Mercury 12B V3.2 by XeyonAI — VRAM 24.5GB, 128K context

Name: Mistral Helcyon Mercury 12B V3.2
Author: XeyonAI

Mistral Helcyon Mercury 12B V3.2 is an open-source language model by XeyonAI. Features: 12b LLM, VRAM: 24.5GB, Context: 128K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.23.

Base model:finetune:mistralai/... Base model:mistralai/mistral-n... Companion Converstional En Gpt-4o Instruct Mistral Region:us Roleplay Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/XeyonAI/Mistral-Helcyon-Mercury-12b-v3.2

Mistral Helcyon Mercury 12B V3.2 Parameters and Internals

LLM Name	Mistral Helcyon Mercury 12B V3.2
Repository 🤗	https://huggingface.co/XeyonAI/Mistral-Helcyon-Mercury-12b-v3.2
Base Model(s)	Mistral Nemo Instruct 2407 mistralai/Mistral-Nemo-Instruct-2407
Model Size	12b
Required VRAM	24.5 GB
Updated	2026-06-04
Maintainer	XeyonAI
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-5 4.9 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 4.9 GB: 5-of-5
Supported Languages	en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.43.0.dev0
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Mistral Helcyon Mercury 12B V3.2 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 Mistral Helcyon Mercury 12B V3.2

Best Alternatives	Context / RAM	Downloads	Likes
...r Nemo 12B Instruct R 21 09 24	1000K / 24.5 GB	396277	141
Qliphoth 12B V1.1	1000K / 24.5 GB	22	3
Qliphoth 12B V1	1000K / 24.5 GB	26	2
SauerkrautLM Nemo 12B Instruct	1000K / 24.5 GB	1473	22
Magnum V4 12B	1000K / 24.5 GB	1116	54
MN Slush	1000K / 24.5 GB	181	32
ChatWaifu V1.4	1000K / 24.5 GB	335	20
ChatWaifu 12B V2.0	1000K / 24.5 GB	175	22
Mistral Nemo Bophades 12B	1000K / 24.5 GB	18	9
Mistral Nemo Wissenschaft 12B	1000K / 24.5 GB	22	8

Note: green Score (e.g. "73.2") means that the model is better than XeyonAI/Mistral-Helcyon-Mercury-12b-v3.2.