Production-oriented demonstration of Multi-LoRA adapter management with sub-3ms hot-swap latency.
Efficient serving of multiple specialized adapters sharing a single base model.
This project demonstrates the Multi-LoRA Serving pattern used in production systems (e.g., LoRAX, vLLM). It allows an inference engine to serve multiple "specialist" models (SQL, Chat, Code) by swapping small LoRA adapters on top of a shared base model nearly instantaneously.
- Note on Adapters: This benchmark uses dummy adapters (random weights) to validate runtime architecture and infrastructure efficiency. It is not intended for semantic output evaluation.
Configuration: Qwen2-0.5B-Instruct (FP16) | Rank-8 LoRA | Median of 10 iterations.
| Metric | Value | Context |
|---|---|---|
| Base Model VRAM | 942.32 MB | FP16 weights |
| Per-Adapter Cost | ~2.07 MB | Rank-8 (q_proj, v_proj) |
| Average Swap Time | 2.30 ms | O(1) pointer updates |
Does adding LoRA layers slow down generation?
| Config | Median Latency (25 tokens) | Overhead vs Base |
|---|---|---|
| Base Only | 438.12 ms | 1.00x |
| Base + LoRA | 445.21 ms | 1.01x |
Insight: Inference overhead is negligible (~1.6%), making Multi-LoRA an extremely efficient way to achieve multi-tenancy.
Multi-LoRA serving is a standard pattern in high-scale LLM infrastructure:
- vLLM: Uses PagedAttention and adapter routing for multi-LoRA support.
- LoRAX: A dedicated framework by Predibase for serving thousands of adapters.
This runtime is part of a larger LLM systems portfolio:
Serving & Runtime:
Kernel-Level Work:
# 1. Install dependencies
pip install -r requirements.txt
# 2. Create dummy adapters locally
python tools/create_local_adapters.py
# 3. Run benchmarks
export PYTHONPATH=.
python benchmarks/latency_memory.pyMIT - Joao Felipe De Souza, 2026