Distributed inference

Extracted Attributes

• Core Concept

  Running AI model inference across multiple interconnected devices, servers, or geographic regions to distribute computational workload.

• Workload Phases

  Prefill (AI) and Decode (AI) workloads.

• Primary Benefits

  Reduced per-request latency, increased throughput, improved reliability through redundancy, and the ability to run models exceeding single-GPU memory limits.

• Parallelism Types

  Pipeline Parallelism (PP) which splits model layers across nodes, and Tensor Parallelism (TP) which splits individual computations within a layer.

Timeline

• Meta releases Llama 3.1, including the 70B and 405B models, which are widely deployed using distributed inference frameworks like vLLM. (Source: Kempner Institute Computing Handbook)

  2024-07-23

• DeepSeek releases DeepSeek-R1 (671B), a massive model requiring distributed inference configurations across multiple GPUs or nodes for efficient serving. (Source: Kempner Institute Computing Handbook)

  2025-01-20

VLLM

vLLM is an open-source software framework for inference and serving of large language models and related multimodal models. Originally developed at the University of California, Berkeley's Sky Computing Lab, the project is centered on PagedAttention, a memory-management method for transformer key–value caches, and supports features such as continuous batching, distributed inference, quantization, and OpenAI-compatible APIs.

Web Search Results

• What is distributed inference?

  Telnyx - Global Communications Platform Provider # What is distributed inference? Distributed inference is the practice of running model inference across multiple machines or geographic regions rather than a single server. Distributed inference is the practice of running model inference across multiple machines or geographic regions rather than a single server. By spreading the computational workload, it reduces per-request latency, increases throughput, and improves reliability through redundancy. This approach is often essential for large language models that exceed the capacity of a single GPU, and for real-time applications that need low-latency responses from nearby compute nodes. [...] If you're scoping a real-time AI workload, explore Telnyx Inference to see pricing and supported models, or talk to our team about your architecture. You can also dig into how we fixed voice AI latency with co-located infrastructure for a concrete example of the approach in production. ## Frequently asked questions What is distributed inference? Distributed inference is running AI model inference across multiple machines or regions rather than a single server. It improves latency, throughput, and reliability by splitting computational work and, when geographically distributed, by routing users to the nearest compute node. [...] The GPU network for distributed compute is where distributed inference either earns its gains or gives them back to overhead. Benchmarks are pending, and the connectivity angle deserves concrete proof points over time, but the architectural logic holds: Fewer hops means fewer billed components. Co-located inference eliminates the cloud-to-carrier round trip that customers pay for on both ends. ## The bottom line Distributed inference isn't a single technique. It's a family of architectural choices for scaling AI across hardware, regions, and networks. Parallelism patterns get the attention, but for real-time applications the network architecture often matters more.

• What Is Distributed Cloud Inference? | Akamai

  Distributed cloud inference refers to the execution of AI and machine learning (ML)models (inference models, pretrained models, foundational models, LLMs) on distributed cloud servers to generate predictions or insights from input data. This process leverages the extensive computational resources and scalable infrastructure provided by cloud platforms. Unlike local or on-device inference — in which models operate directly on the end user’s hardware — cloud inference offloads the computational burden to powerful, specialized GPU servers. When a request for inference is initiated, data is transmitted to the cloud and processed by the deployed model, and the resulting prediction or output is returned to the requesting application or device. This paradigm enables complex models to be utilized [...] High availability and resiliency: Distributed cloud inference does not rely on a single region. Workloads can be routed around failures or congestion to maintain continuous operation. Hybrid and edge deployment alignment:Easily supports architectures where inference must run in the cloud or at the edge, enabling AI wherever data is generated or consumed. [...] Centralized cloud:As previously described, inference execution happens on remote servers in a centralized data center. Advantages: Access to vast computational resources, centralized model management, easier updates, and high scalability. Disadvantages:Latency introduced by network transmission, reliance on stable internet connectivity, and potential data privacy concerns due to data transfer. Distributed cloud inference:Inference execution is performed across a distributed data center and edge platform that brings artificial intelligence (AI)inference closer to end users (at the “edge” of the network). Advantages:Low latency, reduced bandwidth usage, enhanced data privacy (data remains local), and continuous operation even without internet connectivity.

• 12. Distributed Inference — Kempner Institute Computing Handbook

  Kempner Institute Computing Handbook - Home Kempner Institute Computing Handbook - Home Repository Open issue .md # Distributed Inference ## Contents # 12. Distributed Inference# Distributed inference enables large language models (LLMs) to serve predictions efficiently across multiple GPUs or nodes. As models grow in size, ranging from tens to hundreds of billions of parameters, single-GPU deployment becomes impractical. This section introduces how distributed inference is achieved using the open-source library vLLM. ## 12.1. Overview# vLLM is a high-performance inference engine designed to serve large transformer models efficiently. It supports: [...] Pipeline Parallelism (PP): Splits the model layers across GPUs or nodes. Tensor Parallelism (TP): Splits individual computations within a layer. These features allow for scalable, memory-efficient, and high-throughput inference, making it ideal for deploying very large models in production environments or on AI clusters. ## 12.2. Supported Models# The following models have been tested and configured for distributed inference using vLLM on AI clusters: | Model | Model Size | Hugging Face | Deployment Guide | --- --- | | Llama 3.1 | 70B | HF Link | Llama 3.1 Deployment | | Llama 3.1 | 405B | HF Link | Llama 3.1 Deployment | | DeepSeek-R1 | 671B | HF Link | DeepSeek-R1 Deployment | | DeepSeek-R1-0528 | 671B | HF Link | DeepSeek-R1-0528 Deployment |

• What is distributed inference?

  ### I want to learn more about: ### Recommended We'll recommend resources you may like as you browse. Try these suggestions for now. ### Get more with a Red Hat account A subscription may be required for some services. ### Edit avatar Login: Account number: # What is distributed inference? ## Jump to section ## What is distributed inference? Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices. Think of it as the software equivalent of the saying, “many hands make light work.” Why you should care about inference [...] Why you should care about inference Distributed inference supports a system that splits requests across a fleet of hardware, which can include physical and cloud servers. From there, each inference server processes its assigned portion in parallel to create an output. The result is a resilient and observable system for delivering consistent and scalable AI-powered services. Explore Red Hat AI ### The dinner party analogy Imagine you’re hosting a dinner party for 10 people. You want to feed everyone 4 courses: an appetizer, a salad, a main course, and a dessert. But you have only 2 hours to prepare the food. Pulling this off would be nearly impossible on your own—either your final output (food quality) would suffer or your speed would slow. [...] ## Advantages of distributed inference Distributed inference lets you apply LLMs across a wider range of applications. This is because it reduces size and speed limitations of running models on a single piece of hardware. As a result, distributed inference reduces latency (delay) and increases bandwidth (capacity) to support diverse use cases on existing infrastructure. ### Consistent and predictable performance Traditional AI inference is often a "black box" that can lead to unpredictable response times and a poor user experience. With distributed inference, intelligent scheduling analyzes incoming requests and routes them to the optimal hardware. This provides a more reliable and stable user experience. ### Cost management

• Distributed inference - Hugging Face

  Hugging Face's logo Accelerate documentation Distributed inference # Accelerate Hugging Face's logo and get access to the augmented documentation experience to get started # Distributed inference Distributed inference can fall into three brackets: We’re going to go through the first and the last bracket, showcasing how to do each as they are more realistic scenarios. ## Sending chunks of a batch automatically to each loaded model This is the most memory-intensive solution, as it requires each GPU to keep a full copy of the model in memory at a given time. Normally when doing this, users send the model to a specific device to load it from the CPU, and then move each prompt to a different device. A basic pipeline using the `diffusers` library might look something like so: [...] `split_points` `device_map="auto" declares, such as` `or` `num_chunks` determines how the batch will be split and sent to the model itself (so `num_chunks=1` with four split points/four GPUs will have a naive MP where a single input gets passed between the four layer split points) `num_chunks` `num_chunks=1` From here, all that’s left is to actually perform the distributed inference! When passing inputs, we highly recommend to pass them in as a tuple of arguments. Using `kwargs` is supported, however, this approach is experimental. `kwargs` When finished all the data will be on the last process only: