What are the hardware requirements for Vedika?

Vedika requires approximately 789.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Vedika and how large is it?

Vedika is developed by Veda-Labs, a model with 952.4b 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 Vedika?

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

Vedika by Veda-Labs — VRAM 789.2GB

Name: Vedika
Author: Veda-Labs

Vedika is an open-source language model by Veda-Labs. Features: 952.4b LLM, VRAM: 789.2GB, License: apache-2.0, LLM Explorer Score: 0.24.

Audio-text-to-text Conversational Endpoints compatible Eval-results Image-text-to-text Inkling mm model Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Veda-Labs/Vedika

Vedika Parameters and Internals

LLM Name	Vedika
Repository 🤗	https://huggingface.co/Veda-Labs/Vedika
Model Size	952.4b
Required VRAM	789.2 GB
Updated	2026-08-09
Maintainer	Veda-Labs
Model Type	inkling_mm_model
Model Files	20.0 GB: 1-of-108 10.6 GB: 2-of-108 19.8 GB: 3-of-108 19.6 GB: 4-of-108 19.4 GB: 5-of-108 19.6 GB: 6-of-108 19.5 GB: 7-of-108 19.3 GB: 8-of-108 9.8 GB: 9-of-108 19.6 GB: 10-of-108 20.0 GB: 11-of-108 19.3 GB: 12-of-108 9.7 GB: 13-of-108 19.3 GB: 14-of-108 19.4 GB: 15-of-108 19.7 GB: 16-of-108 9.8 GB: 17-of-108 19.7 GB: 18-of-108 9.7 GB: 19-of-108 19.6 GB: 20-of-108 19.9 GB: 21-of-108 10.2 GB: 22-of-108 19.8 GB: 23-of-108 19.5 GB: 24-of-108 19.7 GB: 25-of-108 20.0 GB: 26-of-108 19.5 GB: 27-of-108 19.8 GB: 28-of-108 19.6 GB: 29-of-108 19.3 GB: 30-of-108 10.0 GB: 31-of-108 19.9 GB: 32-of-108 19.5 GB: 33-of-108 19.5 GB: 34-of-108 19.3 GB: 35-of-108 19.9 GB: 36-of-108 19.9 GB: 37-of-108 19.4 GB: 38-of-108 19.4 GB: 39-of-108 19.7 GB: 40-of-108 19.5 GB: 41-of-108 19.9 GB: 42-of-108 19.6 GB: 43-of-108 13.0 GB: 44-of-108
Model Architecture	InklingForConditionalGeneration
License	apache-2.0

Rank the Vedika 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 Vedika

Best Alternatives	Context / RAM	Downloads	Likes
Inkling	0K / 789.2 GB	77799	1699

Note: green Score (e.g. "73.2") means that the model is better than Veda-Labs/Vedika.