[SLMs_vs_LLMs_ANALYSIS]

Understanding the fundamental differences between Small Language Models and Large Language Models in the context of decentralized training infrastructure.

[TECHNICAL_COMPARISON]

METRIC	SLMs (Small)	LLMs (Large)
Parameters	100M - 7B	13B - 175B+
VRAM Required	4-24GB	40-80GB+
Training Time	Hours - Days	Weeks - Months
Inference Speed	Fast (ms)	Slow (seconds)
Specialization	High	General Purpose
Deployment	Edge/Mobile	Cloud/Server

[ARCHITECTURE_OVERVIEW]

SLM_ARCHITECTURE

• Transformer layers: 12-32

• Attention heads: 8-32

• Hidden size: 768-4096

• Vocabulary: 32K-50K tokens

• Context length: 2K-8K

OPTIMAL_FOR: Task-specific applications, real-time inference, resource-constrained environments

LLM_ARCHITECTURE

• Transformer layers: 96-175+

• Attention heads: 96-128

• Hidden size: 12288-20480

• Vocabulary: 50K-100K tokens

• Context length: 8K-32K+

OPTIMAL_FOR: General reasoning, complex tasks, research, high-accuracy applications

[WHY_SLMs_ON_GRADYAN]

ADVANTAGES:

• Lower GPU requirements (RTX 3080+)
• Faster training convergence
• Better for specialized tasks
• More accessible to node operators
• Energy efficient
• Easier to fine-tune

USE_CASES:

• Code generation assistants
• Domain-specific chatbots
• Text classification
• Sentiment analysis
• Language translation
• Content moderation