AIvailable: A Software-Defined Architecture for LLM-as-a-Service on Heterogeneous and Legacy GPUs

TL;DR

AIvailable is a software-defined platform enabling scalable, high-performance LLM inference across heterogeneous and legacy GPUs, optimizing resource utilization and accessibility for resource-constrained environments.

Contribution

It introduces a novel architecture that abstracts GPU heterogeneity and legacy hardware, providing VRAM-aware dynamic allocation for LLM inference without CPU fallback.

Findings

Supports diverse open LLMs on legacy GPUs

Achieves efficient resource utilization and high availability

Enables democratization of generative AI in constrained settings

Abstract

The rise of Large Language Models (LLM) has increased the need for scalable, high-performance inference systems, yet most existing frameworks assume homogeneous, resource-rich hardware, often unrealistic in academic, or resource-constrained settings. We introduce AIvailable, a low-cost, highly available LLM-as-a-Service (LLMaaS) platform, that uses a software-defined approach for running LLMs across heterogeneous and legacy GPU nodes, including NVIDIA and AMD devices, with a focus on fully utilizing each node's VRAM. AIvailable operates as a fully GPU-accelerated inference without CPU fallbacks, featuring a unified client interface that allows seamless interaction with all deployed LLMs through a single logical unit. The architecture comprises four main components: the Client Interface for user access, the Service Frontend for secure request routing and load balancing, the SDAI Controller for orchestration, deployment, and monitoring, and the Service Backend of heterogeneous GPU nodes executing workloads. By abstracting GPU-specific details and providing dynamic, VRAM-aware allocation and reallocation of models, AIvailable ensures efficient use of resources and resilience against failures or workload fluctuations. Targeting academic labs, private companies, and other constrained organizations, it supports diverse open LLMs helping democratize generative AI through the repurposing of legacy GPUs.