What are the hardware requirements for MegaBeam Mistral 7B 512K?

MegaBeam Mistral 7B 512K requires approximately 14.4 GB of VRAM and supports a context window of 512K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MegaBeam Mistral 7B 512K and how large is it?

MegaBeam Mistral 7B 512K is developed by aws-prototyping, a model with 7b 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 MegaBeam Mistral 7B 512K?

MegaBeam Mistral 7B 512K is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MegaBeam Mistral 7B 512K by aws-prototyping — VRAM 14.4GB, 512K context

Name: MegaBeam Mistral 7B 512K
Author: aws-prototyping

MegaBeam Mistral 7B 512K is an open-source language model by aws-prototyping. Features: 7b LLM, VRAM: 14.4GB, Context: 512K, License: apache-2.0, LLM Explorer Score: 0.22.

Arxiv:2505.08651 Conversational Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/aws-prototyping/MegaBeam-Mistral-7B-512k

MegaBeam Mistral 7B 512K Parameters and Internals

Model Type

Large-Context LLM

Training Details

Methodology:

Trained on Mistral-7B Instruct-v0.2

Context Length:

524288

Input Output

Input Format:

Supports 524,288 tokens in context

Accepted Modalities:

text

LLM Name	MegaBeam Mistral 7B 512K
Repository 🤗	https://huggingface.co/aws-prototyping/MegaBeam-Mistral-7B-512k
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-08-04
Maintainer	aws-prototyping
Model Type	mistral
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	524288
Model Max Length	524288
Transformers Version	4.43.3
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the MegaBeam Mistral 7B 512K 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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