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Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Fast, lightweight Python framework for scalable LLM inference
LightLLM delivers high‑speed, scalable LLM serving in pure Python, integrating proven kernels from FasterTransformer, vLLM, FlashAttention, and more, with easy deployment on a single GPU or cluster.
LightLLM is a Python‑centric inference and serving platform designed for researchers and engineers who need high‑throughput LLM deployment without heavyweight dependencies. Its pure‑Python core and token‑level KC cache make it ideal for rapid prototyping of new decoding methods and for integrating with existing Python ML pipelines.
By reusing optimized kernels from FasterTransformer, FlashAttention, and vLLM, LightLLM achieves industry‑leading latency on modern NVIDIA GPUs, exemplified by the fastest DeepSeek‑R1 serving on an H200. The built‑in Past‑Future request scheduler provides SLA‑aware multi‑tenant serving, while the framework scales from a single GPU to multi‑node clusters with minimal configuration.
The project is backed by recent research papers accepted at ACL, ASPLOS, and other top conferences, and it powers several academic and industry systems. Its modular design encourages contributions, and users can join the Discord community for support and collaboration.
When teams consider LightLLM, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Real‑time chat assistant
Delivers sub‑50 ms response latency for LLM‑driven conversational agents on a single H200 GPU.
Batch inference for document summarization
Processes thousands of documents per hour with token‑level caching, reducing redundant computation.
Research on structured generation
Enables deterministic pushdown automata decoding for faster, constrained output in academic experiments.
Multi‑tenant LLM serving with SLA
Uses Past‑Future scheduler to guarantee latency bounds across different client workloads.
LightLLM is optimized for NVIDIA GPUs with Tensor Cores and works with CUDA‑enabled environments; CPU support is limited.
It leverages high‑performance kernels from FasterTransformer, FlashAttention, and vLLM, combined with a token‑level KC cache and an efficient request scheduler.
Yes, it can load models saved in standard Hugging Face or PyTorch checkpoints.
The pure‑Python architecture and modular kernel design allow researchers to plug in new decoding algorithms or cache mechanisms.
Join the Discord community, file issues on GitHub, or submit pull requests; the project follows an Apache‑2.0 license.
Project at a glance
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