🧠
Gemma 2 27b model by google
0
🧠

Gemma 2 27b

by google Model ID: google--gemma-2-27b
FNI 0

"State-of-the-art AI model: gemma-2-27b by google...."

🔗 View Source
27B Params
4k Context
- Downloads
24G GPU ~22GB Est. VRAM

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run gemma-2-27b
🤗 HF Download
huggingface-cli download gemma-2-27b
📊

Engineering Specs

⚡ Hardware

Parameters
27B
Architecture
-
Context Length
4K

🧠 Lifecycle

Library
-
Precision
float16
Tokenizer
-

🌐 Identity

Source
huggingface
License
Open Access
💾

Est. VRAM Benchmark

~21.6GB

Analyze Hardware

* Technical estimation for FP16/Q4 weights. Does not include OS overhead or long-context batching. For Technical Reference Only.

📈 Interest Trend

--

* Real-time activity index across HuggingFace, GitHub and Research citations.

🔍 Semantic Keywords

Semantic tags indexing in progress via Knowledge Mesh...

No similar models found.

đŸ”ŦTechnical Deep Dive

Full Specifications [+]
---

🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run gemma-2-27b
🤗 HF Download
huggingface-cli download gemma-2-27b
đŸ–Ĩī¸

Hardware Compatibility

Multi-Tier Validation Matrix

Live Sync
🎮 Compatible

RTX 3060 / 4060 Ti

Entry 8GB VRAM
🎮 Compatible

RTX 4070 Super

Mid 12GB VRAM
đŸ’ģ Compatible

RTX 4080 / Mac M3

High 16GB VRAM
🚀 Compatible

RTX 3090 / 4090

Pro 24GB VRAM
🏗ī¸ Compatible

RTX 6000 Ada

Workstation 48GB VRAM
🏭 Compatible

A100 / H100

Datacenter 80GB VRAM
ℹī¸

Pro Tip: Compatibility is estimated for 4-bit quantization (Q4). High-precision (FP16) or ultra-long context windows will significantly increase VRAM requirements.

README

Neural Fact Sheet: Gemma 2 27b

[!IMPORTANT] Full Disclosure Protocol Active: Primary source documentation is restricted or gated. The following technical intelligence has been extracted from the R2 Production Node and Zero-Limit Knowledge Mesh.

📊 Core Architecture

  • Parameter Scale: 27B
  • Neural Architecture: Neural Transformer
  • Inference Efficiency: Auditing... (FNI Logic Score)
  • License Profile: Proprietary/Restricted

⚙ī¸ Technical Capabilities

  • Neural Context Window: 4k tokens
  • Memory Footprint: ~22GB (Q4) estimated VRAM
  • Pipeline Origin: Standard AI
  • Safety Status: Model utilizes developer-defined safety filters.

🚀 Strategic Recommendations

  1. Inference Hub: Recommended for local execution via Ollama or vLLM for private infrastructure.
  2. Context Limits: Optimal performance is maintained within the first 4k tokens of input.
  3. Hardware Alignment: Ideal for hardware with at least ~22GB (Q4) of high-speed video memory.

For full unrestricted documentation, please click "View Source" in the header.

ZEN MODE â€ĸ README

📝 Limitations & Considerations

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

Cite this model

Academic & Research Attribution

BibTeX 
@misc{google__gemma_2_27b,
  author = {google},
  title = {undefined Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/google/gemma-2-27b}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
google. (2026). undefined [Model]. Free2AITools. https://huggingface.co/google/gemma-2-27b
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id
google--gemma-2-27b
source
huggingface
author
google
tags

⚙ī¸ Technical Specs

architecture
null
params billions
27
context length
4,096
vram gb
21.6
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)