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 for just-in-time loading without blocking requests, and the ability to merge adapters. It utilizes heterogeneous continuous batching to pack requests for different adapters, optimizing aggregate throughput. LoRAX incorporates advanced inference optimizations such as tensor parallelism, pre-compiled CUDA kernels (flash-attention, paged attention, SGMV), quantization, and token streaming. It's production-ready with prebuilt Docker images, Helm charts for Kubernetes, Prometheus metrics, distributed tracing, and an OpenAI compatible API supporting multi-turn chat. It supports private adapters via per-request tenant isolation and structured output (JSON mode). Supported base models include Llama, Mistral, and Qwen, with adapters trained using PEFT and Ludwig libraries. LoRAX is free for commercial use under the Apache 2.0 License.
Platform
Task
Features
• free for commercial use
• dynamic adapter loading
• heterogeneous continuous batching
• optimized inference
• adapter exchange scheduling
• ready for production
Pricing Plans
Free
Free Plan• Multi-LoRA inference
• Dynamic adapter loading
• Heterogeneous continuous batching
• Optimized inference
• Production-ready tools
• OpenAI compatible API
• Apache 2.0 License
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