TGI Multi-LoRA: Deploy Once, Serve 30 Models

RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks

RW-LoRA introduces a random‑walk approach to fine‑tune LoRA models in a decentralized setting, using a single model token that moves through the network and updates locally. This eliminates the need for global synchronization and reduces communication and computation costs compared to centralized or gossip‑based methods. The authors provide convergence guarantees for non‑convex objectives and demonstrate competitive performance on NLP tasks across various graph topologies.

Deploy Meta Llama 3.1 405B on Google Cloud Vertex AI

K-Merge: Online Continual Merging of Adapters for On-device Large Language Models

arXiv:2510. 13537v2 Announce Type: replace-cross Abstract: On-device deployment of Large Language Models (LLMs) frequently leverages Low-Rank Adapters (LoRAs) to support diverse downstream tasks under tight resource constraints.

PLoRA: Efficient Concurrent LoRA Training for Large Language Models

arXiv:2508. 02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and good performance.

Making thousands of open LLMs bloom in the Vertex AI Model Garden

Talaria: Session-Aware Serverless Serving of Hundred-Billion-Parameter LLMs

arXiv:2607. 17181v1 Announce Type: cross Abstract: Serverless multi-model LLM systems multiplex popularity-skewed model catalogs over shared GPU pools, yet typically schedule each request independently.