LoRAX
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About
LoRAX (LoRA eXchange) is a powerful framework designed for serving thousands of fine-tuned Large Language Models (LLMs) on a single GPU. It significantly reduces serving costs while maintaining high throughput and low latency. Key features include dynamic adapter loading from HuggingFace, Predibase, or local files, allowing just-in-time loading without blocking requests, and the ability to merge adapters per request for powerful ensembles. It employs heterogeneous continuous batching to pack requests for different adapters, ensuring consistent latency and throughput. LoRAX also optimizes performance with adapter exchange scheduling, asynchronously prefetching and offloading adapters between GPU and CPU memory, and uses optimized inference techniques like tensor parallelism, pre-compiled CUDA kernels (flash-attention, paged attention, SGMV), quantization, and token streaming. It's production-ready with Docker images, Helm charts, Prometheus metrics, Open Telemetry, and an OpenAI compatible API supporting multi-turn chat and structured output. LoRAX supports base models like Llama, Mistral, and Qwen, which can be loaded in fp16 or quantized. It supports LoRA adapters trained using PEFT and Ludwig libraries.
Platform
Task
Features
• free for commercial use
• dynamic adapter loading
• heterogeneous continuous batching
• optimized inference
• adapter exchange scheduling
• ready for production
FAQs
What is LoRAX?
LoRAX (LoRA eXchange) is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency.
Pricing Plans
Apache 2.0 License
Free Plan• Dynamic Adapter Loading
• Heterogeneous Continuous Batching
• Adapter Exchange Scheduling
• Optimized Inference
• Ready for Production
• Full commercial use
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