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Llama 4 Scout 17b 16e Instruct Quantized.w4a16 model by RedHatAI
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Model

Llama 4 Scout 17b 16e Instruct Quantized.w4a16

by RedHatAI redhatai/llama-4-scout-17b-16e-instruct-quantized.w4a16
Free2AITools Nexus Index
57.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 46
P: Popularity 43
R: Recency 100
Q: Quality 65
Tech Context
109.44 Params
8.192K Ctx
Vital Performance
9.1K DL / 30D
Source β†’
Low FNI signal 57 FNI Score
Massive 109.44B Params
8k Context
9.1K Downloads
H100+ ~85GB Est. VRAM
Dense LLAMA4FORCONDITIONALGENERATION Architecture
Restricted LLAMA4 License
Model Information Summary
Entity Passport
Registry ID redhatai/llama-4-scout-17b-16e-instruct-quantized.w4a16
License llama4
Provider huggingface
πŸ’Ύ

Compute Threshold

~84.6GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization. [Multi-GPU / Unified Memory Required]

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{redhatai_llama_4_scout_17b_16e_instruct_quantized_w4a16,
  author = {RedHatAI},
  title = {Llama 4 Scout 17b 16e Instruct Quantized.w4a16 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16}},
  note = {Accessed via Free2AITools.}
}
APA Style
RedHatAI. (2026). Llama 4 Scout 17b 16e Instruct Quantized.w4a16 [Model]. Free2AITools. https://huggingface.co/RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download redhatai/llama-4-scout-17b-16e-instruct-quantized.w4a16

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 46
Popularity (P) 43
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Llama 4 Scout 17b 16e Instruct Quantized.w4a16: Authority (A:46), Popularity (P:43), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Technical Deep Dive

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
9.1KDownloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--redhatai--llama-4-scout-17b-16e-instruct-quantized.w4a16
slug
redhatai--llama-4-scout-17b-16e-instruct-quantized.w4a16
source
huggingface
author
RedHatAI
license
llama4
tags
safetensors, llama4, facebook, meta, pytorch, llama, neuralmagic, redhat, llmcompressor, quantized, w4a16, int4, conversational, compressed-tensors, image-text-to-text, ar, de, en, es, fr, hi, id, it, pt, th, tl, vi, license:llama4, region:us

βš™οΈ Technical Specs

architecture
Llama4ForConditionalGeneration
params billions
109.44
context length
8,192
pipeline tag
image-text-to-text
vram gb
84.6
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
9,127
stars
null
forks
null

Data indexed from public sources. Updated daily.