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How do local llms handle context management and tokenisation

Local LLMs and cloud LLMs share similar fundamental principles for token handling and context management, but there are important differences in their implementation and limitations.

Token Processing

Local LLMs break down text into tokens just like cloud-based models, with tokens representing words, parts of words, or individual characters[2]. The tokenization process is essential for text processing and directly impacts:

  • Model performance
  • Memory usage
  • Processing speed
  • Cost efficiency (when applicable)

Context Window Considerations

Memory and Resource Usage

The context window in local LLMs faces significant hardware constraints:

  • GPU memory usage and running time scale linearly with the KV (Key-Value) cache size[1]
  • Processing long contexts requires substantial computational resources
  • Memory requirements increase quadratically with context length[6]

Performance Impact

Local LLMs experience several performance-related effects with longer contexts:

  • Speed vs Context Length: Larger context windows result in slower response times[6]
  • Missing Middle Problem: Models often struggle with accurately recalling information from the middle of long contexts[6]
  • Attention Patterns: Models show better performance at the beginning (primary bias) and end (recency bias) of the context[6]

Hardware Requirements

Running local LLMs with large context windows requires significant hardware resources:

Multi-GPU Setups:

  • 2x RTX 4090: Initial cost $4,000, monthly maintenance \~$150[3]
  • 4x RTX 4090: Initial cost $8,000, monthly maintenance \~$200[3]

Optimization Techniques

Recent developments have introduced methods to handle long contexts more efficiently:

  • GemFilter: A novel approach that can reduce input tokens by up to 1000x while maintaining performance[1]
  • KV Cache Optimization: Techniques for compressing and managing the Key-Value cache to improve memory efficiency[1]
  • Token Selection: Algorithms that identify and retain the most relevant tokens while discarding less important ones[1]

Practical Implications

When choosing between local and cloud LLMs, consider:

  • Control: Local deployment offers more control over the model and data processing[4]
  • Latency: Local models can provide faster response times for certain applications[4]
  • Scalability: Local setups have limited scalability compared to cloud solutions[4]
  • Privacy: Running locally ensures data stays within your infrastructure[4]

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.