What are the hardware requirements for Sambanova Llama2 100?

Sambanova Llama2 100 requires approximately 13.9 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Sambanova Llama2 100 and how large is it?

Sambanova Llama2 100 is developed by cello78, a model with 6.9b 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 Sambanova Llama2 100?

Sambanova Llama2 100 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Sambanova Llama2 100 by cello78 — VRAM 13.9GB, 4K context

Name: Sambanova Llama2 100
Author: cello78

Sambanova Llama2 100 is an open-source language model by cello78. Features: 6.9b LLM, VRAM: 13.9GB, Context: 4K, LLM Explorer Score: 0.18.

Arxiv:1910.09700 Conversational Endpoints compatible Llama Llama-factory Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/cello78/sambanova-llama2-100

Sambanova Llama2 100 Parameters and Internals

LLM Name	Sambanova Llama2 100
Repository 🤗	https://huggingface.co/cello78/sambanova-llama2-100
Model Size	6.9b
Required VRAM	13.9 GB
Updated	2026-07-27
Maintainer	cello78
Model Type	llama
Model Files	3.9 GB: 1-of-4 4.0 GB: 2-of-4 4.0 GB: 3-of-4 2.0 GB: 4-of-4
Model Architecture	LlamaForCausalLM
Context Length	4096
Model Max Length	4096
Transformers Version	4.45.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	57344
Torch Data Type	bfloat16

Rank the Sambanova Llama2 100 capabilities

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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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