What are the hardware requirements for Mergekit Passthrough Vsvihrr?

Mergekit Passthrough Vsvihrr requires approximately 45.9 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 Mergekit Passthrough Vsvihrr and how large is it?

Mergekit Passthrough Vsvihrr is developed by mergekit-community, a model with 22.9b 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 Mergekit Passthrough Vsvihrr?

Mergekit Passthrough Vsvihrr is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mergekit Passthrough Vsvihrr by mergekit-community — VRAM 45.9GB, 1000K context

Name: Mergekit Passthrough Vsvihrr
Author: mergekit-community

Mergekit Passthrough Vsvihrr is an open-source language model by mergekit-community. Features: 22.9b LLM, VRAM: 45.9GB, Context: 1000K, Merged, LLM Explorer Score: 0.17.

Merged Model Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mergekit-community/mergekit-passthrough-vsvihrr

Mergekit Passthrough Vsvihrr Parameters and Internals

LLM Name	Mergekit Passthrough Vsvihrr
Repository 🤗	https://huggingface.co/mergekit-community/mergekit-passthrough-vsvihrr
Base Model(s)	Mergekit Model Stock Tiwlqms mergekit-community/mergekit-model_stock-tiwlqms
Merged Model	Yes
Model Size	22.9b
Required VRAM	45.9 GB
Updated	2026-08-07
Maintainer	mergekit-community
Model Type	mistral
Model Files	4.9 GB: 1-of-10 5.0 GB: 2-of-10 4.9 GB: 3-of-10 4.9 GB: 4-of-10 4.9 GB: 5-of-10 4.9 GB: 6-of-10 5.0 GB: 7-of-10 4.9 GB: 8-of-10 4.9 GB: 9-of-10 1.6 GB: 10-of-10
Model Architecture	MistralForCausalLM
Context Length	1024000
Model Max Length	1024000
Transformers Version	4.49.0
Tokenizer Class	PreTrainedTokenizer
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Mergekit Passthrough Vsvihrr 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 Mergekit Passthrough Vsvihrr

Best Alternatives	Context / RAM	Downloads	Likes
Mergekit Slerp Wzipxtu	1000K / 45.7 GB	5	0
Mergekit Passthrough Romewyb	1000K / 45.9 GB	7	0
...l Small Instruct 2409 Bnb 4bit	128K / 12.1 GB	1111	4

Note: green Score (e.g. "73.2") means that the model is better than mergekit-community/mergekit-passthrough-vsvihrr.