DistServe

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DistServe is an open-source system that improves Large Language Model (LLM) serving performance. It disaggregates prefill and decoding computation, reducing interference and allowing independent resource allocation.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is DistServe?

DistServe is an open-source, disaggregated serving system designed to enhance the performance of Large Language Models (LLMs). Unlike traditional systems that colocate prefill and decoding computations, DistServe separates these phases. This innovative approach significantly reduces prefill-decoding interferences and allows for independent resource allocation and parallelism configurations for each phase. Users can easily set parallelism and scheduling strategies, with the system automatically managing KV-Cache communication and memory. It leverages SwiftTransformer, a high-performance C++ inference library, supporting features like model/pipeline parallelism, FlashAttention, Continuous Batching, and PagedAttention. DistServe supports popular models including GPT-2, OPT, and LLaMA2, and relies on Ray for distributed workers.

Best used for

Ideal for developers who need to optimize the serving performance of large language models, manage distributed LLM deployments, and customize parallelism and scheduling strategies. Especially valuable for reducing inference latency and improving throughput in LLM applications.

Common actions

optimize LLM serving

manage distributed LLMs

configure LLM parallelism

deploy LLM inference

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Capabilities

Key features

Disaggregated prefill/decoding
Independent resource allocation
Customizable parallelism configs
KV-Cache communication management
SwiftTransformer inference backend
Supports GPT-2, OPT, LLaMA2
Ray-based distributed workers

Target Audience

developer

Integrations

ray

Pricing & Plans

Open Source

Free

FAQs

What LLM models does DistServe support?

DistServe supports a range of popular Large Language Models, including GPT-2 (e.g., gpt2, gpt2-xl), OPT (e.g., facebook/opt-1.3b, facebook/opt-6.7b), and LLaMA2 (e.g., meta-llama/Llama-2-7b, meta-llama/Llama-2-13b). This allows for broad compatibility with common LLM architectures.

How does DistServe handle distributed inference?

DistServe leverages Ray to implement distributed workers for inference. If a Ray runtime isn't pre-launched, it automatically initiates a cluster using all GPUs on the current node. For multi-node inference, users need to manually start the Ray runtime beforehand to ensure proper distribution.

What are the minimum hardware requirements to run DistServe?

To run DistServe, particularly for its offline inference examples, a minimum of two GPUs is required. This setup allows the system to effectively demonstrate its disaggregated prefill and decoding capabilities and manage the computational load.

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