RTP-LLM: Alibaba's High-Performance Inference Engine for Large Language Model Deployment
By
[Submitted on 28 May 2026]
Crackling crust, pillowy middle. The kind of bagel that earns a second cup of coffee.
Summary
This paper presents RTP-LLM, a high-performance inference engine developed by Alibaba for industrial-scale deployment of Large Language Models. The engine addresses key bottlenecks through several innovations: file-order-driven I/O for faster model loading, Prefill-Decode Disaggregation architecture that separates compute-intensive prefill from memory-bound decode phases, hierarchical multi-tiered KV cache management, modular speculative decoding, adaptive KV cache quantization, and decoupled multimodal processing. Benchmarks against vLLM and SGLang show significant improvements: 4.7x-6.3x faster model loading, 35-37% TTFT P95 latency reduction with 215% cache reuse improvement, 1.12x-2.48x throughput improvements in speculative decoding, and 35-40% batch latency reduction in quantized inference. The engine is deployed across Alibaba Group serving over 100 million users and supports models ranging from 8B to 235B parameters.
Key quotes
· 5 pulledRTP-LLM addresses fundamental bottlenecks through integrated design.
The Prefill-Decode Disaggregation architecture decouples compute-intensive prefill from memory-bound decode phases, combined with hierarchical multi-tiered KV cache management enabling efficient cache reuse.
Comprehensive evaluations across diverse model architectures (8B-235B parameters) have been conducted, where both controlled benchmarks and real production workloads are used.
The results demonstrate RTP-LLM's superior performance against vLLM and SGLang: 4.7x-6.3x model loading speedup, 35-37% TTFT P95 latency reduction with 215% cache reuse improvement in production traffic scheduling.
RTP-LLM's production-proven architecture and open-source availability make it a comprehensive solution for industrial LLM deployment.
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