Characterizing the Behavior and Impact of KV Caching on Transformer Inferences Under Concurrency

Pre-training of LLMs and transformers is known to take weeks if not months even with powerful HPC systems. However, inferences are an equally important problem: once pre-trained, the model needs to serve a large number of inferences submitted under concurrency by multiple users. Thus, speeding up each inference request is instrumental in achieving high throughput and latency at scale. To avoid redundant recomputation in each decode iteration, a Key-Value (KV) cache is used to store previously computed keys (K) and values (V), speeding up token generation. GPU memory is primarily consumed by model weights and the remainder is used by the KV cache. Thus, the free GPU space available to the KV cache is a scarce resource that needs to be managed in an efficient way in order to minimize the overhead of redundant recomputations. There are many optimizations applied in this context: batching of inference requests to enable them to run in the same forward pass (and thus increase the parallelism and inference throughput), different KV cache eviction policies (simply drop KV entries and recompute them later vs. swap to host memory), etc. Under these circumstances, the decision of what batching strategy, what KV cache eviction policy to apply and how the KV cache impacts the inference performance is non-trivial. Unlike the case of pre-training, state-of-art studies are scarce in this context. To fill this gap, in this paper we study the impact of KV caching. Specifically, we instrument vLLM to measure and analyze fine-grain metrics (token throughput, KV cache memory access patterns, load balancing of the forward passes), during different inference stages (prefill, decode) in several scenarios that involve concurrent inference requests using several benchmarks. Based on the measurements and associated observations, we identify several opportunities to improve the design of inference frameworks.