Make one table to compare all the difference of requirements between all Llama models.

Llama 3.1 8B Requirements
Category	Requirement	Details
Llama 3.1 8B Model Specifications

Parameters

8 billion
Context Length

128K tokens
Multilingual Support	8 languages
Hardware Requirements

CPU and RAM	
CPU: Modern processor with at least 8 cores.
RAM: Minimum of 16 GB recommended.
GPU

NVIDIA RTX 3090 (24 GB) or RTX 4090 (24 GB) for 16-bit mode.
Storage	Disk Space: Approximately 20-30 GB for the model and associated data.
Estimated GPU Memory Requirements

Higher Precision Modes

32-bit Mode: ~38.4 GB
16-bit Mode: ~19.2 GB
Lower Precision Modes	
8-bit Mode: ~9.6 GB
4-bit Mode: ~4.8 GB
Software Requirements	Operating System	Linux or Windows (Linux preferred for better performance).
Software Dependencies

Programming Language: Python 3.7 or higher.
Frameworks: PyTorch (preferred) or TensorFlow.
Libraries: Hugging Face Transformers, NumPy, Pandas.
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Llama 3.1 70B Requirements
Category	Requirement	Details
Llama 3.1 70B Model Specifications

Parameters

70 billion
Context Length	128K tokens
Multilingual Support	8 languages
Hardware Requirements	CPU and RAM	
CPU: High-end processor with multiple cores.
RAM: Minimum of 32 GB, preferably 64 GB or more.
GPU	GPU Options:
2-4 NVIDIA A100 (80 GB) in 8-bit mode.
8 NVIDIA A100 (40 GB) in 8-bit mode.
Storage	Disk Space: Approximately 150-200 GB for the model and associated data.
Estimated GPU Memory Requirements	Higher Precision Modes	
32-bit Mode: ~336 GB
16-bit Mode: ~168 GB
Lower Precision Modes	
8-bit Mode: ~84 GB
4-bit Mode: ~42 GB
Software Dependencies	Additional Requirements	Same as the 8B model but may require additional configurations for optimized performance.
 Download Llama 3.1
Llama 3.1 405B Requirements
Category	Requirement	Details
Llama 3.1 405B Model Specifications	Parameters	405 billion
Context Length	128K tokens
Multilingual Support	8 languages
Hardware Requirements	CPU and RAM	
CPU: High-performance server processors with multiple cores.
RAM: Minimum of 128 GB, preferably 256 GB or more.
GPU	GPU Options:
8 AMD MI300 (192 GB) in 16-bit mode.
8 NVIDIA A100/H100 (80 GB) in 8-bit mode.
4 NVIDIA A100/H100 (80 GB) in 4-bit mode.
Storage	Disk Space: Approximately 780 GB for the complete model and associated data.
Estimated GPU Memory Requirements	Higher Precision Modes	
32-bit Mode: ~1944 GB
16-bit Mode: ~972 GB
Lower Precision Modes	
8-bit Mode: ~486 GB
4-bit Mode: ~243 GB
Software Dependencies	Additional Requirements	
Advanced configurations for distributed computing.
May require additional software like NCCL for GPU communication.


Llama 3.2 1B Instruct Requirements
Category	Requirement	Details
Llama 3.2 1B Instruct Model Specifications	Parameters	1 billion
Context Length	128,000 tokens
Multilingual Support	8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
Hardware Requirements	CPU and RAM	
CPU: Multicore processor
RAM: Minimum of 16 GB recommended
GPU	NVIDIA RTX series (for optimal performance), at least 4 GB VRAM
Storage	Disk Space: Sufficient for model files (specific size not provided)
Estimated GPU Memory Requirements	Higher Precision Modes	
BF16/FP16: ~2.5 GB
Lower Precision Modes	
FP8: ~1.25 GB
INT4: ~0.75 GB
Software Requirements	Operating System	Compatible with cloud, PC, and edge devices
Software Dependencies	
Programming Language: Python 3.7 or higher
Frameworks: PyTorch
Libraries: Hugging Face Transformers, CUDA, TensorRT (for NVIDIA optimizations)
 Download Llama 3.2
Llama 3.2 3B Instruct Requirements
Category	Requirement	Details
Llama 3.2 3B Instruct Model Specifications	Parameters	3 billion
Context Length	128,000 tokens
Multilingual Support	8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
Hardware Requirements	CPU and RAM	
CPU: Multicore processor
RAM: Minimum of 16 GB recommended
GPU	NVIDIA RTX series (for optimal performance), at least 8 GB VRAM
Storage	Disk Space: Sufficient for model files (specific size not provided)
Estimated GPU Memory Requirements	Higher Precision Modes	
BF16/FP16: ~6.5 GB
Lower Precision Modes	
FP8: ~3.2 GB
INT4: ~1.75 GB
Software Requirements	Operating System	Compatible with cloud, PC, and edge devices
Software Dependencies	
Programming Language: Python 3.7 or higher
Frameworks: PyTorch
Libraries: Hugging Face Transformers (version 4.45.0 or higher), CUDA
 Download Llama 3.2
Llama 3.2 11B Vision Requirements
Category	Requirement	Details
Model Specifications	Parameters	11 billion
Context Length	128,000 tokens
Image Resolution	Up to 1120×1120 pixels
Multilingual Support	8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
Hardware Requirements	GPU	
High-end GPU with at least 22GB VRAM for efficient inference
Recommended: NVIDIA A100 (40GB) or A6000 (48GB)
Multiple GPUs can be used in parallel for production
CPU	High-end processor with at least 16 cores (AMD EPYC or Intel Xeon recommended)
RAM	Minimum: 64GB, Recommended: 128GB or more
Storage	NVMe SSD with at least 100GB free space (22GB for model)
Software Requirements	Operating System	Linux (Ubuntu 20.04 LTS or higher) or Windows with optimizations
Frameworks and Libraries	PyTorch 2.0+, CUDA 11.8+, cuDNN 8.7+
Development Environment	Python 3.8+, Anaconda/Miniconda
Additional Libraries	transformers, accelerate, bitsandbytes, einops, sentencepiece
Deployment Considerations	Cloud Services	Available on Amazon SageMaker JumpStart and Amazon Bedrock
Container	Docker containers recommended for deployment
Optimizations	Quantization	Support for 4-bit quantization to reduce memory requirements
Parallelism	Model parallelism techniques for multi-GPU distribution
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Llama 3.2 90B Vision Requirements
Category	Requirement	Details
Model Specifications	Parameters	90 billion
Context Length	128,000 tokens
Image Resolution	Up to 1120×1120 pixels
Multilingual Support	8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
Hardware Requirements	GPU	
High-end GPU with at least 180GB VRAM to load the full model
Recommended: NVIDIA A100 with 80GB VRAM or higher
For inference: Multiple lower-capacity GPUs can be used in parallel
CPU	
High-end processor with at least 32 cores
Recommended: Latest generation AMD EPYC or Intel Xeon
RAM	
Minimum: 256GB system RAM
Recommended: 512GB or more for optimal performance
Storage	
NVMe SSD with at least 500GB free space
Approximately 180GB required just to store the model
Software Requirements	Operating System	
Linux (Ubuntu 20.04 LTS or higher recommended)
Windows support with specific optimizations
Frameworks and Libraries	
PyTorch 2.0 or higher
CUDA 11.8 or higher
cuDNN 8.7 or higher
Development Environment	
Python 3.8 or higher
Anaconda or Miniconda for virtual environment management
Additional Libraries	
transformers (Hugging Face)
accelerate
bitsandbytes (for quantization)
einops
sentencepiece
Deployment Considerations	Container	Docker containers recommended for deployment and dependency management
Cloud Services	Suggested use of cloud services like Amazon SageMaker or Google Cloud AI Platform for production inference
Optimizations	Quantization	Support for 4-bit quantization to reduce memory requirements
Parallelism	Implementation of model parallelism techniques to distribute load across multiple GPUs