Cache-Aware Request Placement is a request routing and scheduling technique used in distributed large language model (LLM) serving infrastructure that optimizes the placement of decode requests by considering the availability and locality of Key-Value (KV) Cache across multiple processing nodes. This approach significantly improves cache hit rates, reduces redundant computation, and enhances overall system throughput in multi-tenant serving scenarios.

In distributed LLM serving systems, the KV Cache—which stores pre-computed key and value tensors for previously processed tokens—represents a critical resource that can be reused across multiple decode requests. Traditional request routing strategies often treat cache location as a secondary concern, leading to suboptimal placement decisions that result in cache misses and unnecessary recomputation of attention operations.

Cache-Aware Request Placement addresses this challenge by making intelligent routing decisions at the request level, considering where relevant cached KV pairs already exist within the cluster. When a new decode request arrives, the scheduler evaluates available nodes not merely based on computational capacity, but also based on the presence of useful cached information from previous requests in the same batch or conversation context ¹⁾. This locality-first approach can dramatically reduce memory bandwidth requirements and improve latency for repeated or related inference patterns.

The cache-aware placement mechanism operates at several levels within the serving infrastructure. At the core, a metadata tracking system maintains information about which KV Cache entries exist on each node, along with their size, age, and access patterns. When a decode request arrives specifying a particular model and context, the placement algorithm queries this metadata to identify nodes with existing cache entries that overlap with the current request's needs.

The placement decision typically follows these steps: First, the system identifies candidate nodes that already contain relevant KV Cache data for the requested context. Second, it evaluates these candidates based on multiple factors including current computational load, remaining memory capacity, and cache hit potential. Third, it performs a cost-benefit analysis weighing the savings from reusing cache against the overhead of transferring the request to a potentially less-loaded node. Finally, it routes the request to the node that maximizes overall throughput, considering both immediate execution cost and system-wide cache efficiency ²⁾.

Several design patterns have emerged in production systems. Sticky routing maintains affinity between requests in the same conversation and specific nodes, encouraging cache reuse. Predictive placement uses patterns from previous requests to pre-stage cache on nodes expected to handle future requests. Cost-aware routing explicitly models the tradeoff between cache hit rates and load balancing, using reinforcement learning or dynamic programming to optimize placement decisions ³⁾.

Effective cache-aware placement requires careful management of several technical challenges. Cache eviction policies must balance the retention of useful cached data against the need to accommodate new requests and prevent memory overflow. Least-Recently-Used (LRU) and Least-Frequently-Used (LFU) strategies are common, though more sophisticated policies can estimate the value of cached entries based on likelihood of future reuse ⁴⁾.

Multi-model scenarios complicate placement decisions when different models with incompatible cache formats operate within the same cluster. Systems must maintain separate cache pools per model and potentially allocate cluster resources dynamically based on traffic patterns.

Consistency and correctness require careful handling of cache invalidation when model weights are updated or when serving different versions of the same model. Distributed consensus mechanisms or centralized cache metadata services track version information to prevent serving requests with stale cached data.

Latency overhead from cache metadata lookups and placement decision-making must be minimized, typically through in-memory data structures and efficient search algorithms such as B-trees or hash tables indexed by context hash or request ID patterns.

Cache-aware placement delivers substantial benefits in several serving scenarios. In multi-turn conversation systems, where the same user engages in multiple exchange turns with a model, earlier tokens remain in cache and can be reused, reducing latency and computational cost for subsequent turns. In batch serving with overlapping prefixes, requests processing common input sequences can share cached computations. In speculative decoding scenarios, preliminary output generation creates cache that can accelerate subsequent refinement passes ⁵⁾.

Production deployments report cache hit rate improvements of 30-70% depending on workload characteristics, translating directly to throughput improvements and reduced tail latency. For cost-sensitive applications, the reduction in redundant computation can decrease per-request inference costs by 20-40%.

Cache-aware placement faces several practical constraints. Heterogeneous hardware environments with varying memory capacities and computational capabilities complicate optimal placement decisions. Dynamic workloads with unpredictable request patterns and context lengths make predictive placement less effective. Memory fragmentation from variable-sized cache entries can reduce effective cache utilization even with sophisticated placement strategies.

The cold-start problem affects newly deployed services or models where no cached data exists yet, requiring systems to gracefully degrade to traditional load-balancing approaches. Cross-cluster scenarios in geographically distributed systems must balance cache reuse against network latency costs, a problem with no universally optimal solution.

Recent work continues to refine cache-aware placement through machine learning-based optimization, exploring reinforcement learning approaches that learn placement policies from traffic patterns. Research also investigates compression-aware placement, where cached data is selectively compressed to increase cache capacity and improve hit rates. Adaptive cache granularity techniques adjust the unit of cache placement from full-sequence to token-level or semantic-segment granularity based on observed reuse patterns.

Emerging directions include integration with serving infrastructure APIs that expose cache information to applications for application-aware optimization, and federated cache management where multiple independent serving systems share cache information across organizational boundaries.

• Memory Caching
• Bandwidth-Aware Scheduling
• Prompt Caching
• Caching Strategies for Agents
• KV Cache Optimization