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LLMServe avatar

LLMServe/DistServe

0
View on GitHub↗
820 stars·95 forks·Jupyter Notebook·Apache-2.0·2 views

DistServe

Disaggregated serving system for Large Language Models (LLMs).

Features

  • Disaggregated Serving - Disaggregates prefill and decoding for optimized serving goodput.

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Open-source alternatives to DistServe

Similar open-source projects, ranked by how many features they share with DistServe.
  • ai-dynamo/dynamoai-dynamo avatar

    ai-dynamo/dynamo

    6,112View on GitHub↗

    Dynamo is a distributed inference orchestration platform designed for large language models. It functions as a system to coordinate prefill and decode phases across GPU nodes, utilizing a multi-backend runtime adapter to connect engines like vLLM and TensorRT-LLM through a unified block-oriented memory interface. An OpenAI-compatible API server provides the frontend for integration with existing tools and clients. The project is distinguished by its disaggregated serving architecture, which separates prompt processing and token generation onto independent GPU pools to optimize throughput and

    Rust
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  • llm-d/llm-dllm-d avatar

    llm-d/llm-d

    2,514View on GitHub↗

    llm-d is a distributed serving framework designed for large language model inference. It functions as an inference orchestrator and gateway, providing a control plane for deploying model replicas and managing hardware accelerators. The system includes a batch inference scheduler and a cache manager to coordinate request flow and memory utilization. The project is distinguished by a disaggregated serving architecture that separates prefill and decode execution phases across specialized workers to maximize throughput. It employs a hardware-agnostic control plane and tiered cache offloading, mov

    Shell
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  • kvcache-ai/mooncakekvcache-ai avatar

    kvcache-ai/Mooncake

    5,594View on GitHub↗

    Mooncake is a disaggregated large language model serving platform and distributed key-value store designed for high-performance inference infrastructure. It functions as a GPU memory orchestrator and KV cache management system that pools and transfers key-value caches across clusters to accelerate inference. The system distinguishes itself by separating the prefill and decode phases of inference into distinct hardware clusters to optimize resource utilization. It utilizes a high-performance RDMA distributed cache with zero-copy transfers to move data between compute nodes, bypassing the CPU t

    C++
    View on GitHub↗5,594

Frequently asked questions

What does llmserve/distserve do?

Disaggregated serving system for Large Language Models (LLMs).

What are the main features of llmserve/distserve?

The main features of llmserve/distserve are: Disaggregated Serving.

What are some open-source alternatives to llmserve/distserve?

Open-source alternatives to llmserve/distserve include: ai-dynamo/dynamo — Dynamo is a distributed inference orchestration platform designed for large language models. It functions as a system… llm-d/llm-d — llm-d is a distributed serving framework designed for large language model inference. It functions as an inference… kvcache-ai/mooncake — Mooncake is a disaggregated large language model serving platform and distributed key-value store designed for…