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Memory Bottleneck from KV Cache in LLMs
During inference, the Key-Value (KV) cache grows linearly with the length of the input sequence. While this is more efficient than quadratic growth, the memory footprint for extremely long sequences can become so significant that it makes the deployment of LLMs for such tasks infeasible. This memory consumption is a primary bottleneck for applying standard Transformers to long-context problems.
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References
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.5 Inference - Foundations of Large Language Models
Related
Memory Bottleneck from KV Cache in LLMs
An auto-regressive language model is generating text and has already produced a sequence of 100 tokens. To generate the 101st token, it must calculate self-attention. If the model stores the 'key' and 'value' vectors for the first 100 tokens, which of the following best describes the computational steps required for the self-attention mechanism at this specific step?
Optimizing Chatbot Inference Speed
Computational Cost of Autoregressive Generation
Learn After
Architectural Adaptation of LLMs for Long Sequences
Linear Attention
Classification of Memory Models in LLMs
Memory Models in LLMs as Context Encoders
PagedAttention for KV Cache Memory Optimization
Strategies for Mitigating KV Cache Memory Usage
A machine learning engineer is deploying a large language model and finds that the system frequently runs out of memory during inference. They are investigating two specific high-load scenarios, both of which involve processing a total of 16,000 tokens:
- Scenario X: Processing a batch of 32 user requests simultaneously, where each request has a context length of 500 tokens.
- Scenario Y: Processing a single user request that involves summarizing a very long document with a context length of 16,000 tokens.
Based on how attention states (keys and values) are managed during inference, which statement best analyzes the memory consumption issue?
Architectural Shift in LLMs due to Long-Sequence Limitations
Diagnosing Inference Failures with Long Documents
Analyzing Memory Constraints in Different LLM Applications