The "pretrain-then-finetune" paradigm is commonly adopted in the deployment
of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient
fine-tuning method, is often employed to adapt a base model to a multitude of
tasks, resulting in a substantial collection of LoRA adapters derived from one
base model. We observe that this paradigm presents significant opportunities
for batched inference during serving. To capitalize on these opportunities, we
present S-LoRA, a system designed for the scalable serving of many LoRA
adapters. S-LoRA stores all adapters in the main memory and fetches the
adapters used by the currently running queries to the GPU memory. To
efficiently use the GPU memory and reduce fragmentation, S-LoRA proposes
Unified Paging. Unified Paging uses a unified memory pool to manage dynamic
adapter weights with different ranks and KV cache tensors with varying sequence
lengths. Additionally, S-LoRA employs a novel tensor parallelism strategy and
highly optimized custom CUDA kernels for heterogeneous batching of LoRA
computation. Collectively, these features enable S-LoRA to serve thousands of
LoRA adapters on a single GPU or across multiple GPUs with a small overhead.
Compared to state-of-the-art libraries such as HuggingFace PEFT and vLLM (with
naive support of LoRA serving), S-LoRA can improve the throughput by up to 4
times and increase the number of served adapters by several orders of
magnitude. As a result, S-LoRA enables scalable serving of many task-specific
fine-tuned models and offers the potential for large-scale customized
fine-tuning services.

通过使用 S-LoRA 系统，可以在单个 GPU 上或多个 GPU 上以较小的开销提高吞吐量并使 LoRA 适配器的数量成倍增加，从而实现可伸缩的运行多个 Fine-tuned 模型的服务。