Memory Fragmentation in LLM Inference
During the process of generating text, language models continuously allocate and deallocate memory, particularly for the KV cache. This dynamic memory usage can lead to fragmentation, where the available memory is split into numerous small, non-contiguous blocks. The diagram visualizes this with interspersed used and free memory blocks. This fragmentation poses a significant challenge, as it can prevent the allocation of large, contiguous memory chunks needed for new or growing sequences, thereby reducing system efficiency.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
Related
Iteration in Continuous Batching
General Process of Continuous Batching
Example of Interleaving Prefilling and Decoding in Continuous Batching
Overhead of Dynamic Batch Reorganization in Continuous Batching
Memory Fragmentation in LLM Inference
Prefilling-Prioritized Strategy in Continuous Batching
Simple Iteration-level Scheduling
Priority-Based Scheduling in LLM Inference
Custom Priority Policies in LLM Scheduling
Disaggregation of Prefilling and Decoding using Pipelined Engines
Comparison of Continuous (Prefilling-Prioritized) vs. Standard (Decoding-Prioritized) Batching
LLM Inference Scheduling Strategy
An LLM inference server is processing a batch of three long-running requests. In the middle of this process, after several computational steps have already been completed for the initial batch, a new, short request arrives. How would a system implementing continuous batching most likely handle this new request in the next computational step?
An LLM inference system is designed to maximize hardware utilization. Which of the following operational descriptions best illustrates the core principle of continuous batching, distinguishing it from a static batching approach?
Memory Fragmentation in LLM Inference
Comparison of Memory Allocation in Standard vs. Paged Attention
Diagnosing Inference Server Failures
An inference server running a large language model processes thousands of text generation requests, each with a different sequence length. The server allocates memory for the key and value vectors of each sequence as a single, contiguous block. After some time, the server begins to fail when trying to allocate memory for new requests, despite system monitoring tools showing that a significant total amount of memory is still free. Which statement best analyzes the most likely reason for these allocation failures?
Drawbacks of Contiguous Memory Allocation for KV Caching