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1. production-stack

   K8S-native cluster-wide deployment for vLLM. Provides a reference implementation for building an inference stack on top of vLLM, enabling scaling, monitoring, request routing, and KV cache offloading with easy cloud deployment.

   2025-04-28 Tags: vllm, kubernetes, inference, deployment, scaling, monitoring, request routing, kv cache, cloud, inference engineering, production engineering, llm by klotz
2. vLLM Production Stack: reference stack for production vLLM deployment

   vLLM Production Stack provides a reference implementation on how to build an inference stack on top of vLLM, allowing for scalable, monitored, and performant LLM deployments using Kubernetes and Helm.

   2025-04-28 Tags: vllm, kubernetes, helm, llm, inference, deployment, observability, kv cache, scalability, production engineering, inference engineering by klotz
3. LLM inference optimization - KV Cache

   The article discusses how the Key-Value (KV) Cache is used to optimize the inference process of Large Language Models (LLMs) by reducing redundant computations and improving performance.

   2024-12-27 Tags: llm, kv cache, self-attention by klotz
4. Techniques for KV Cache Optimization in Large Language Models

   This post explores optimization techniques for the Key-Value (KV) cache in Large Language Models (LLMs) to enhance scalability and reduce memory footprint, covering methods like Grouped-query Attention, Sliding Window Attention, PagedAttention, and distributed KV cache across multiple GPUs.

   2024-12-27 Tags: llm, kv cache, grouped-query attention, sliding window attention, pagedattention by klotz

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