When deploying models fine-tuned with parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA), you can either keep LoRA adapters separate or merge their weights into the backbone model. Merging folds the adapter parameters into the model, creating a single file and removing the need for custom adapter logic at inference. This simplifies deployment but may slightly increase model size, as backbone weights are permanently modified. Keeping adapters separate maintains modularity but adds deployment complexity.

PEFT methods like LoRA add minimal overhead to inference, as the extra low-rank computations are small when the adapter rank $r$ is much less than the model size $N$. For example, a 100-million-parameter model with LoRA rank 8 adds only a tiny fraction of extra parameters and memory use. Keeping adapters separate can slightly increase latency due to extra memory reads, while merging adapters into the backbone removes this overhead and matches the original model's speed. Throughput is typically unaffected unless memory limits or adapter management cause delays.

Serving PEFT models in production requires careful management. Batching requests boosts hardware utilization, but you must handle adapter selection efficiently if inputs use different adapters. Quantization — reducing precision to save memory and speed up inference — works well with PEFT, but always quantize backbone and adapter weights together. To support multiple adapters without duplicating the backbone, dynamically load and apply the right adapter per request, or use merged models for common cases. Robust adapter management is essential to prevent latency spikes and memory fragmentation.

Below is an ASCII diagram illustrating a typical serving pipeline for PEFT models using adapters and quantized weights:

In this pipeline, the incoming request is first analyzed to determine which adapter (if any) should be loaded. The backbone model is quantized for efficiency, and the adapter weights are applied before running inference. The result is then returned to the user.

Key insights for deploying PEFT models include:

• Merge LoRA weights into the backbone to simplify deployment and minimize runtime complexity;
• Quantize both backbone and adapter weights together to maintain accuracy and efficiency;
• Batch requests carefully, ensuring adapter selection does not introduce contention or latency;
• Dynamically load adapters to support multiple users or tasks without duplicating the backbone;
• Monitor memory usage to avoid fragmentation when handling many adapters in production.