SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation

LLM inference for popular enterprise use cases, such as summarization, RAG, and code-generation, typically observes orders of magnitude longer prompt lengths than generation lengths. This characteristic leads to high cost of prefill and increased response latency. In this paper, we present SwiftKV, a novel model transformation and distillation procedure specifically designed to reduce the time and cost of processing prompt tokens while preserving high quality of generated tokens. SwiftKV combines three key mechanisms: i) SingleInputKV, which prefills later layers' KV cache using a much earlier layer's output, allowing prompt tokens to skip much of the model computation, ii) AcrossKV, which merges the KV caches of neighboring layers to reduce the memory footprint and support larger batch size for higher throughput, and iii) a knowledge-preserving distillation procedure that can adapt existing LLMs for SwiftKV with minimal accuracy impact and low compute and data requirement. For Llama-3.1-8B and 70B, SwiftKV reduces the compute requirement of prefill by 50% and the memory requirement of the KV cache by 62.5% while incurring minimum quality degradation across a wide range of tasks. In the end-to-end inference serving using an optimized vLLM implementation, SwiftKV realizes up to 2 higher aggregate throughput and 60% lower time per output token. It can achieve a staggering 560 TFlops/GPU of normalized inference throughput, which translates to 16K tokens/s for Llama-3.1-70B in 16-bit precision on 4 H100 GPUs. While it is clear that LLMs can add value to these applications, the cost and speed of inference determine their practicality. Therefore, improving the aggregate throughput and reducing latency of LLM inference has become an increasingly important topic of interest, with various efforts (Sec. In this paper, we take a unique approach to improving LLM inference for enterprise applications based on the key observation that typical enterprise workloads process many more input tokens than output tokens. For example, tasks like code completion, text-to-SQL, summarization and RAG each submit long prompts but produce a small number of generated tokens, and a majority of enterprise LLM use cases in Snowflake incur a 10:1 ratio between prompt and generated tokens. After distillation, the KV cache of layers 5-8 can all be populated using the hidden state outputs of layer 4. For prefill tokens, the query, attention, and MLP operations of layers 5-8 may be skipped altogether, while decode tokens complete all layers. Existing models may be efficiently adapted for SwiftKV by distilling from the original unmodified model using a small dataset.