#memory-management

#memory-management

Swap behaves differently in Kubernetes than in traditional Linux server environments.

Enabling swap alone does not prevent OOMKills or pod evictions in Kubernetes environments.

Kubernetes handles memory management and swap differently than traditional Linux, leading to potential failures when using swap.

MindsDB streamlines the creation of AI agents by overcoming challenges related to legacy data systems and complex queries.

Go 1.24 introduces runtime.AddCleanup and weak.Pointer for enhanced garbage collection management.

Lyft engineers efficiently manage iOS app extension development by optimizing dependencies, binary size, and memory usage while adhering to Apple's constraints.

Rust offers improved type safety and memory management through its ownership model.

Restricted Algorithmic Rust serves as a bridge from C/C++ to RAC while maintaining Rust's proper syntax.

vLLM outperforms other models in handling higher request rates while maintaining low latencies through efficient memory management.

vLLM optimizes large language model serving through innovative memory management and GPU techniques.

PagedAttention optimizes memory usage in language model serving, significantly improving throughput while minimizing KV cache waste.

Memory sharing in PagedAttention enhances efficiency in LLMs, significantly reducing memory usage during sampling and decoding processes.

PagedAttention optimizes memory usage in LLM serving by managing key-value pairs in a non-contiguous manner.

vLLM optimizes memory management in LLM decoding by reserving only necessary resources, improving efficiency and performance.

Anchor-based LLMs reduce memory use by 99% while improving inference speed by up to 3.5 times, enabling practical use on resource-constrained devices.

vLLM significantly improves throughput in LLM tasks by utilizing shared prefixes among different input prompts.

vLLM was tested using various Transformer-based large language models to evaluate its performance under load.

Transformer-based language models use autoregressive approaches for token sequence probability modeling.

Batching improves compute utilization for LLMs, but naive strategies can cause delays and waste resources. Fine-grained batching techniques offer a solution.

Large Language Models often exceed single GPU limits, requiring advanced distributed execution techniques for memory management.

vLLM utilizes FCFS scheduling and an all-or-nothing eviction policy to effectively manage resources and prioritize fairness in request handling.

Efficient memory management and tensor contiguity are essential for optimizing performance in PyTorch, especially when handling large-scale datasets.

Current LLM serving systems inefficiently manage memory, resulting in significant waste due to fixed size allocations based on potential maximum sequence lengths.

The final Android 15 beta focuses on stable APIs for developer use and introduces significant behavior changes and new privacy features.

Android developers must prepare for a 16 KB memory page size upgrade to gain performance benefits of 5-10% in apps and games.

Linked lists are efficient for fast addition of data without resizing the entire array, suitable for write-only data, and organizing data for sequential reads.

Data structures are key to efficient data storage and organization, crucial for memory management and optimizing software performance.