What are the hardware requirements for Mistral Nemo Instruct 2407 FP8?

Mistral Nemo Instruct 2407 FP8 requires approximately 13.6 GB of VRAM and supports a context window of 1000K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mistral Nemo Instruct 2407 FP8 and how large is it?

Mistral Nemo Instruct 2407 FP8 is developed by RedHatAI, a model with 12.2b 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 Nemo Instruct 2407 FP8?

Mistral Nemo Instruct 2407 FP8 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mistral Nemo Instruct 2407 FP8 by RedHatAI — VRAM 13.6GB, 1000K context

Name: Mistral Nemo Instruct 2407 FP8
Author: RedHatAI

Mistral Nemo Instruct 2407 FP8 is an open-source language model by RedHatAI. Features: 12.2b LLM, VRAM: 13.6GB, Context: 1000K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.16.

Conversational En Endpoints compatible Fp8 Instruct Mistral Region:us Safetensors Sharded Tensorflow Vllm

Model Card on HF 🤗: https://huggingface.co/RedHatAI/Mistral-Nemo-Instruct-2407-FP8

Mistral Nemo Instruct 2407 FP8 Parameters and Internals

LLM Name	Mistral Nemo Instruct 2407 FP8
Repository 🤗	https://huggingface.co/RedHatAI/Mistral-Nemo-Instruct-2407-FP8
Model Size	12.2b
Required VRAM	13.6 GB
Updated	2026-08-11
Maintainer	RedHatAI
Model Type	mistral
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 3.7 GB: 3-of-3
Supported Languages	en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	1024000
Model Max Length	1024000
Transformers Version	4.43.0.dev0
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Mistral Nemo Instruct 2407 FP8 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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...istral Nemo Instruct 2407 MPOA	1000K / 24.5 GB	37	3
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Kalypso	1000K / 24.5 GB	45	24
Crimson Dawn V0.2	1000K / 24.5 GB	17	13
Azure Dusk V0.2	1000K / 24.5 GB	43	10
...al Nemo Japanese Instruct 2408	1000K / 24.5 GB	2358	49
Educa Ai Nemo Sft	1000K / 49.3 GB	13	4
...l Nemo Abliterated Nemo Pro V2	1000K / 24.5 GB	19	1

Note: green Score (e.g. "73.2") means that the model is better than RedHatAI/Mistral-Nemo-Instruct-2407-FP8.