What are the hardware requirements for Apertus 8B MeditronFO?

Apertus 8B MeditronFO requires approximately 16.1 GB of VRAM and supports a context window of 64K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Apertus 8B MeditronFO and how large is it?

Apertus 8B MeditronFO is developed by EPFLiGHT, a model with 8b 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 Apertus 8B MeditronFO?

Apertus 8B MeditronFO is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Apertus 8B MeditronFO by EPFLiGHT — VRAM 16.1GB, 64K context

Name: Apertus 8B MeditronFO
Author: EPFLiGHT

Apertus 8B MeditronFO is an open-source language model by EPFLiGHT. Features: 8b LLM, VRAM: 16.1GB, Context: 64K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.28.

Arxiv:2605.16215 Apertus Base model:finetune:swiss-ai/a... Base model:swiss-ai/apertus-8b... Clinical Conversational Dataset:epflight/fully-open-me... En Endpoints compatible Fully-open Healthcare Instruct Medical Medical-llm Meditron Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/EPFLiGHT/Apertus-8B-MeditronFO

Apertus 8B MeditronFO Parameters and Internals

LLM Name	Apertus 8B MeditronFO
Repository 🤗	https://huggingface.co/EPFLiGHT/Apertus-8B-MeditronFO
Base Model(s)	swiss-ai/Apertus-8B-Instruct-2509 swiss-ai/Apertus-8B-Instruct-2509
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-08-02
Maintainer	EPFLiGHT
Model Type	apertus
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 4.9 GB: 2-of-4 5.0 GB: 3-of-4 1.2 GB: 4-of-4
Supported Languages	en
Model Architecture	ApertusForCausalLM
License	apache-2.0
Context Length	65536
Model Max Length	65536
Transformers Version	4.57.1
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
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Data Analysis and Insight Generation
Text Generation
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Multi-Language Support and Translation

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