Distributed Scheduling for AI Workloads: An Architectural Analysis of Ray and Hugging Face TGI

Executive Summary

This report provides a comprehensive architectural analysis of two leading frameworks in the artificial intelligence (AI) ecosystem: Ray and Hugging Face Text Generation Inference (TGI). The central inquiry revolves around their respective approaches to distributed scheduling, a critical function for deploying scalable and efficient AI applications, particularly those leveraging large language models (LLMs). The analysis reveals that Ray and TGI are not direct competitors but rather complementary technologies occupying different architectural strata. The core thesis of this report is that Ray is a general-purpose distributed compute engine and a programmable control plane, while TGI is a specialized, high-performance inference engine. Consequently, the decision between them is not one of substitution but of defining their distinct roles within a modern AI platform architecture.

The key findings of this investigation are threefold. First, the frameworks are built on fundamentally different scheduling philosophies. Ray employs a “bottom-up” distributed scheduler that is resource-centric, designed to provide maximum flexibility and resource awareness for a diverse range of heterogeneous workloads across a multi-node cluster.1 In contrast, TGI’s scheduling is request-centric, meticulously engineered around the principle of continuous batching to maximize the computational throughput and utilization of GPUs for the singular task of LLM token generation.3

Second, this philosophical difference dictates their primary architectural roles. Ray, particularly through its native library Ray Serve, provides a robust framework for building and orchestrating entire end-to-end AI applications. Its Python-native, programmable nature makes it ideal for composing complex multi-stage pipelines, such as Retrieval-Augmented Generation (RAG) systems, and multi-agent workflows where business logic and multiple model calls are intertwined.5 TGI, conversely, is an optimized and largely opaque service designed to execute one function with maximum efficiency: converting input prompts into output token streams at scale. It is a purpose-built inference server, not a general application development framework.7

Third, the most powerful and increasingly prevalent production pattern is a hybrid architecture that leverages the strengths of both. In this paradigm, Ray Serve functions as a high-level, flexible control plane that orchestrates one or more TGI or vLLM instances, which serve as the high-performance data plane. This approach combines Ray’s sophisticated routing, composition, and application management capabilities with TGI’s state-of-the-art inference performance, offering a robust and scalable solution for complex AI services.8

Based on this analysis, this report offers a clear strategic recommendation. For organizations seeking to deploy simple, high-throughput model endpoints with minimal operational overhead, TGI presents a strong, out-of-the-box solution. However, for the development of complex, multi-component AI applications that require the integration of business logic, model composition, A/B testing, or dynamic routing, Ray Serve is the superior architectural choice. In these advanced scenarios, TGI should be viewed not as an alternative to Ray, but as a performance-critical component to be orchestrated by Ray.

 

Foundational Paradigms in Distributed AI Execution

 

The advent of large-scale AI models has fundamentally reshaped the requirements for distributed systems. Modern AI applications, especially those powered by LLMs, are rarely monolithic, “tensor-in, tensor-out” services. Instead, they are increasingly complex, multi-stage computational graphs that blend machine learning inference with traditional business logic. A typical RAG application, for instance, involves a sequence of distinct operations: receiving a user query, preprocessing the input, querying a vector database, retrieving and ranking relevant documents, constructing a context-rich prompt, calling an LLM for generation, and finally, post-processing the output before returning it to the user.5 This inherent complexity demands a new way of thinking about the architecture of AI serving platforms, moving beyond simple model deployment to holistic application orchestration.

To effectively analyze and compare frameworks like Ray and TGI, it is essential to introduce a conceptual model that separates the distinct responsibilities within an AI serving system: the Control Plane and the Data Plane. This architectural distinction provides a powerful lens through which to understand the unique value proposition of each technology.

• The Control Plane is the orchestration layer of the system. It is responsible for the high-level logic that governs the application’s behavior. Its duties include request ingress and validation, dynamic routing (e.g., for A/B testing or canary deployments), execution of business logic, composition of multiple models into a coherent pipeline, management of autoscaling policies, and providing system-wide observability and monitoring. In essence, the control plane orchestrates what computations need to happen and when they should be executed.
• The Data Plane is the execution layer. It is responsible for performing the raw, computationally intensive tasks at the heart of the application. In the context of LLM serving, the data plane’s primary function is the high-performance execution of the transformer model’s forward pass—the matrix multiplications and attention calculations that generate tokens. The data plane is optimized for one thing: executing how a specific computation is performed with maximum speed and efficiency.

This separation of concerns is not merely an academic exercise; it reflects a significant trend in the AI infrastructure industry. Early model serving solutions often attempted to bundle control and data plane functionalities into a single, monolithic system. However, the extreme performance demands of LLMs—requiring custom CUDA kernels, sophisticated memory management techniques like PagedAttention, and advanced batching strategies—led to the emergence of a new class of highly specialized inference engines. Frameworks like TGI, vLLM, and NVIDIA’s TensorRT-LLM are exemplars of this trend. They are pure data plane technologies, intentionally narrow in scope and meticulously optimized for the singular purpose of LLM inference.8

The rise of these specialized engines created an architectural vacuum. While they solved the problem of raw inference performance, they were not designed to handle the complexities of application-level orchestration. They do not provide native tools for building RAG pipelines, implementing A/B testing logic, or composing multiple distinct models. This gap necessitated a corresponding evolution in control plane technology. A flexible, programmable, and distributed orchestration layer was needed to manage these powerful but specialized inference engines.

It is within this architectural context that Ray and TGI find their respective roles. TGI is a quintessential data plane technology, a hyper-optimized inference engine. Ray, and specifically its library Ray Serve, is fundamentally a control plane technology. It provides the Python-native, distributed primitives necessary to build and orchestrate the entire application graph, within which a specialized engine like TGI can be plugged in as a performance-critical component. Therefore, the user’s query of “Ray or TGI” is more strategically framed as, “How should a modern AI application be architected, and what are the distinct and complementary roles that Ray and TGI play within that architecture?” This reframing shifts the discussion from a simple feature comparison to a more nuanced exercise in sophisticated systems design.

 

Architectural Deep Dive: Ray’s General-Purpose Distributed Scheduling

 

Ray is an open-source, unified framework designed to scale any AI or Python application from a laptop to a large cluster with minimal code changes.9 Its power lies in a simple yet profound set of core primitives and a sophisticated, decentralized scheduling architecture that is built for generality, scalability, and fault tolerance. Unlike specialized systems, Ray’s scheduler is not designed for a single type of workload; rather, it provides a foundational layer upon which diverse and complex distributed applications can be built.10

 

The Ray Core Engine: Primitives for Distributed Python

 

At the heart of Ray is a small set of abstractions that extend Python’s familiar programming model to a distributed setting. These primitives—Tasks, Actors, and Objects—are the building blocks for all higher-level libraries in the Ray ecosystem, including Ray Serve.9

• Tasks: A Ray Task is simply a Python function executed remotely and asynchronously. By applying the @ray.remote decorator to a standard Python function, a developer transforms it into a stateless computation that can be scheduled anywhere on the Ray cluster. When a remote function is called with .remote(), it immediately returns an ObjectRef (a future) and begins executing in the background on a worker process. This model is ideal for data processing, simulations, and other “embarrassingly parallel” workloads where computations can be performed independently.9
• Actors: A Ray Actor is the stateful counterpart to a Task. By applying the @ray.remote decorator to a Python class, a developer creates a stateful worker process. Each instance of an actor class is created on a specific node in the cluster and can be invoked by other tasks or actors through an actor handle. Method calls on an actor are executed serially on that specific process, ensuring that its internal state is managed correctly. Actors are essential for any distributed pattern that requires maintaining state over time, such as holding a loaded machine learning model in memory, managing a database connection pool, or acting as a parameter server.2 The replicas within a Ray Serve deployment are implemented as Ray Actors.
• Objects: Ray Objects are immutable values stored in Ray’s distributed, in-memory object store. When a task executes, its return value is placed in the object store, and an ObjectRef is returned to the caller. This ObjectRef is a future that can be passed as an argument to other tasks or actor methods. Ray’s scheduler uses the locations of these objects to make intelligent, locality-aware scheduling decisions. The object store, known as the Plasma store, uses shared memory on each node, which allows for zero-copy reads between different worker processes on the same machine, enabling highly efficient data sharing and transfer.13

 

The ‘Bottom-Up’ Distributed Scheduling Mechanism

 

To manage millions of fine-grained tasks per second with millisecond latencies, Ray employs a decentralized, hierarchical scheduling architecture. This design deliberately avoids the bottlenecks associated with traditional, centralized cluster schedulers and is a key enabler of Ray’s performance and scalability.2 The architecture consists of two primary components: the Global Control Store (GCS) and the per-node Raylet.

• Component Roles:

• Global Control Store (GCS): The GCS is the centralized source of truth for all system-level metadata in the Ray cluster. It is a fault-tolerant key-value store, typically backed by Redis, that runs on the head node. The GCS maintains critical information such as the liveness of each node, the total and available resources on each node, the physical location of all actors, and the lineage of all objects (for fault tolerance). A crucial design principle of Ray is that all other system components are stateless; they can fail and be restarted, recovering their necessary state from the GCS. This design greatly simplifies the implementation of fault tolerance across the system.15
• Raylet: A raylet is a daemon process that runs on every node in the cluster, including the head node. Each raylet serves two primary functions: it acts as a local scheduler for tasks submitted on its node, and it serves as a resource manager for the resources (CPUs, GPUs, memory) of that node. The raylet “owns” its node’s resources and is responsible for leasing them to worker processes to execute tasks and actors.19

• The Scheduling Flow (Bottom-Up): The term “bottom-up” describes the path a scheduling request takes through this hierarchical system.

1. When a worker process needs to execute a task, it submits a resource request to the raylet on its local node.14
2. The local raylet first attempts to satisfy the request using its own node’s resources. This local-first approach is fundamental to Ray’s low-latency performance, as it avoids a network round-trip to a global scheduler for the majority of tasks. The raylet’s decision is guided by a strong preference for data locality; it will prioritize scheduling the task on a node where its input ObjectRefs are already stored in the local object store, thereby minimizing data transfer over the network.1
3. If the local node cannot fulfill the request—either because it is overloaded or because it lacks the necessary resources (e.g., a task requires a GPU but the node is CPU-only)—the local raylet forwards the scheduling request to the GCS on the head node.14
4. The GCS, possessing a global view of the entire cluster’s resource availability, acts as a global scheduler. It identifies a set of feasible nodes that can satisfy the task’s requirements and selects the best candidate, often based on load-balancing heuristics. The GCS then communicates this placement decision back to the originating raylet, which subsequently directs the task to the chosen target node’s raylet for execution.19

This bottom-up flow ensures that the global scheduler is not a bottleneck. It is only invoked when necessary, allowing the system to achieve extremely high scheduling throughput and low latency for the common case of locally satisfiable tasks.

 

Advanced Scheduling Strategies

 

Ray’s scheduling system is not a monolithic, one-size-fits-all solution. It is designed as a flexible foundation that provides powerful, low-level primitives, allowing developers and higher-level libraries to implement sophisticated scheduling policies. This design follows the philosophy of an exokernel, an operating system concept where the kernel provides minimal, secure mechanisms for hardware multiplexing, while leaving complex resource management policies to user-level libraries.11 In Ray, the GCS and raylets provide the core mechanisms for resource accounting and task placement, while libraries like Ray Tune and Ray Serve implement the policies that are best suited for their specific workloads.

This approach manifests in several advanced scheduling features:

• Placement Groups: This is a powerful API for reserving a bundle of resources (e.g., 8 GPUs and 64 CPUs) across potentially multiple nodes. Ray guarantees that this bundle of resources will be placed atomically, a concept known as gang scheduling. This is indispensable for workloads like distributed model training, where all training workers must be co-scheduled and start simultaneously to establish communication channels.1
• Label-Based Scheduling and Node Affinity: Developers can exert fine-grained control over task and actor placement using labels. Ray automatically assigns default labels to nodes (e.g., ray.io/node-id, ray.io/accelerator-type: ‘A100’), and users can add custom labels (e.g., ‘region’: ‘us-east-1’). Tasks and actors can then specify affinity to these labels, ensuring they are scheduled only on nodes that meet specific criteria. This can be a “hard” requirement (the task fails if no such node is available) or a “soft” preference.1
• Integration with External Schedulers (NVIDIA KAI): For enterprise-grade workload management, Ray, when deployed on Kubernetes via the KubeRay operator, can integrate with external, more sophisticated schedulers like the NVIDIA KAI Scheduler. This integration unlocks advanced capabilities that are not native to Ray’s default scheduler, such as true gang scheduling for distributed workloads, hierarchical queuing to manage resource allocation between different teams or projects, and priority-based preemption. With preemption, a high-priority, latency-sensitive inference job can automatically reclaim GPU resources from a long-running, lower-priority training job, ensuring that critical services remain responsive without manual intervention.21

This “exokernel” philosophy is the source of Ray’s versatility. It is not a single, opinionated scheduler but a foundational compute engine upon which many different and even conflicting scheduling policies can be built. This stands in stark contrast to TGI’s single-purpose, highly optimized scheduling design and is the key to Ray’s ability to serve as a universal framework for distributed AI.

 

Architectural Deep Dive: Hugging Face TGI’s Inference-Centric Scheduling

 

Hugging Face Text Generation Inference (TGI) is a toolkit engineered from the ground up for a single, demanding purpose: deploying and serving LLMs in production with high performance and efficiency.22 Its architecture and scheduling mechanisms are not designed for general-purpose distributed computing; instead, every component is meticulously optimized to maximize throughput and minimize latency for text generation workloads. TGI’s design philosophy prioritizes performance through specialized techniques, making it a powerful data plane component in a modern AI stack.

 

TGI’s Service-Oriented Architecture

 

TGI is architected as a set of distinct, communicating components that work in concert to handle inference requests. While typically deployed within a single container, these components have separate responsibilities, allowing for a clean separation of concerns between request handling and model computation.7

• Component Breakdown:

• Router (Webserver): The public face of the TGI service is a high-performance web server written in Rust. This component acts as the entry point for all incoming traffic, accepting HTTP requests through both a custom API and standard OpenAI-compatible endpoints (e.g., /v1/chat/completions). The Router’s most critical responsibility is to perform the core scheduling logic. It receives individual requests, buffers them in a queue, and applies sophisticated strategies to group them into optimal batches for the model server. This batching logic is the heart of TGI’s scheduling system.7
• Launcher: The launcher is a helper script that orchestrates the startup of the TGI service. It is responsible for initializing the environment, downloading model weights from the Hugging Face Hub, and launching one or more instances of the model server. If tensor parallelism is used to shard a large model across multiple GPUs, the launcher manages the creation of a model server process for each shard. Once the model servers are ready, the launcher starts the Router, providing it with the necessary gRPC connection information to communicate with the model servers.7
• Model Server: The model server is a Python process responsible for the actual computation. It loads the model weights into GPU memory and exposes a gRPC interface. It receives batched requests from the Router, performs the forward pass of the transformer model to generate the next tokens, and streams the results back to the Router. In a multi-GPU setup, each model server instance holds a shard of the model, and they coordinate during inference using libraries like NCCL.7

 

The Core of TGI Performance: Continuous Batching

 

The single most important scheduling technique employed by TGI to achieve high throughput is continuous batching. This approach directly addresses a fundamental inefficiency in serving autoregressive models like LLMs.3

• The Problem with Naive Batching: In traditional deep learning, static batching (waiting for a fixed number of requests) or dynamic batching (waiting for a time window) is effective because all inputs in a batch typically require a similar amount of computation. However, this assumption breaks down for LLM inference. The total computation time for a request depends on both its prompt length and its generated output length, which can vary dramatically from one request to the next. In a naively batched system, the entire batch must wait for the longest-running sequence to complete before the next batch can begin. This leads to significant periods where the GPU is underutilized or completely idle, as it waits for the final tokens of a single long response while other, shorter requests in the same batch have already finished.4
• Continuous Batching Explained: Continuous batching, also known as in-flight batching or iteration-level scheduling, solves this problem by decoupling the batch’s lifecycle from the lifecycle of individual requests. The process works as follows:

1. The TGI router assembles a batch of requests from its queue, up to the maximum capacity of the GPU memory.
2. It sends this batch to the model server, which performs a single decoding step, generating one new token for every sequence in the batch.
3. The router inspects the results. If any sequence has finished (i.e., it has generated an end-of-sequence token), it is immediately evicted from the batch.
4. The now-vacant slot in the batch is instantly filled with a new request from the waiting queue.
5. This newly constituted batch is then sent back to the model server for the next decoding step.
   This cycle repeats for every token generation step, ensuring that the batch size remains at or near its maximum capacity at all times. This dynamic recomposition of the batch keeps the GPU’s parallel processing units constantly supplied with work, eliminating the “bubbles” of idle time inherent in other batching methods.4

• Impact on Performance: The implementation of continuous batching is the primary driver of TGI’s impressive throughput. By maximizing GPU utilization, it can lead to dramatic performance improvements. Benchmarks have shown that continuous batching can provide up to an 8x increase in throughput compared to naive static batching for typical LLM workloads, making it a non-negotiable feature for any production-grade inference server.25

 

Optimizations for Throughput and Memory

 

Beyond continuous batching, TGI integrates several other state-of-the-art optimizations to further enhance performance and memory efficiency.

• PagedAttention and FlashAttention: TGI leverages highly optimized implementations of the attention mechanism, which is the most computationally intensive part of a transformer model. FlashAttention is a custom CUDA kernel that reorders the attention computation to reduce the number of reads and writes to high-bandwidth memory (HBM), significantly speeding up the process.3 PagedAttention, a technique pioneered by the vLLM project and adopted by TGI, is a memory management innovation for the KV cache. Instead of allocating a single, contiguous block of memory for each sequence’s KV cache, PagedAttention allocates memory in smaller, non-contiguous blocks, or “pages.” This approach, analogous to virtual memory in operating systems, dramatically reduces internal memory fragmentation and enables more efficient sharing of memory between requests, allowing the system to handle larger batches and achieve higher throughput.3
• Tensor Parallelism: For deploying models that are too large to fit into the memory of a single GPU, TGI supports tensor parallelism. This technique shards the model’s weight matrices across multiple GPUs within a single node. During the forward pass, each GPU computes its portion of the result, and the results are synchronized across GPUs using high-speed interconnects like NVLink. This allows TGI to serve models with hundreds of billions of parameters by distributing the memory and compute load.7
• Quantization: To reduce the memory footprint and potentially increase inference speed, TGI supports various quantization techniques, such as loading models in 8-bit or 4-bit precision using libraries like bitsandbytes and GPT-Q. This allows larger models to be deployed on hardware with less VRAM.22

 

Multi-Backend Architecture

 

A significant recent evolution in TGI’s design is its move towards a modular, multi-backend architecture. Recognizing that the optimal low-level inference implementation can vary depending on the model architecture, hardware, and specific workload, the TGI team has refactored its core. By introducing a Backend trait (an interface) in its Rust-based router, TGI can now be configured to route requests to different underlying inference engines while presenting a consistent, stable API to the end-user.28

This strategic shift positions TGI not just as a single implementation but as a standardized, enterprise-ready frontend for high-performance inference. It allows Hugging Face to integrate best-in-class backends—such as vLLM for its PagedAttention implementation, TensorRT-LLM for peak performance on NVIDIA GPUs, or llama.cpp for CPU-optimized inference—under a unified TGI umbrella. This decouples the concerns of production-readiness (e.g., observability, API compatibility, safety features) from the rapidly evolving landscape of low-level kernel optimization. For users, this means they can rely on TGI’s stable interface while benefiting from the best available performance for their specific deployment target.

 

Ray Serve: A Control Plane for Complex AI Applications

 

While Ray Core provides the fundamental building blocks for distributed computing, Ray Serve is the specialized library within the Ray ecosystem designed for building and deploying online inference APIs.5 However, to categorize Ray Serve as merely a “model server” is to miss its primary value proposition. Ray Serve is a programmable and scalable control plane for building entire end-to-end ML-powered applications. It leverages the power of Ray’s core primitives to allow developers to express complex, multi-component services as simple, composable Python code.5

 

The Architecture of a Ray Serve Application

 

A Ray Serve application is constructed from several types of Ray Actors, each with a specific role in managing the application’s lifecycle and handling requests. This actor-based architecture is the key to its scalability and fault tolerance.29

• Component Roles:

• Controller: For each Ray Serve instance, there is a single, global Controller actor. This actor is the brain of the control plane. It is responsible for managing the lifecycle of the entire application, including creating, updating, and destroying other actors. When a developer deploys a new application or updates an existing one, the API call is routed to the Controller, which then orchestrates the necessary changes across the cluster. The Controller checkpoints the application’s state (e.g., deployment configurations, routing policies) to Ray’s GCS, enabling recovery in case of failure.29
• HTTP/gRPC Proxy: These actors are the entry points for external traffic. A proxy runs a web server (Uvicorn for HTTP, grpcio for gRPC) and listens for incoming requests. Upon receiving a request, it looks up the appropriate deployment and forwards the request to one of its replicas. For high availability and scalability, proxies can be deployed on every node in the cluster, allowing traffic to be load-balanced across them.29
• Replicas: Replicas are the Ray actors that execute the user’s application code. A Deployment in Ray Serve is a logical grouping of identical replicas that can be scaled horizontally. Each replica can, for example, load an instance of an ML model into memory and define the logic for processing requests. Replicas process requests from the proxy and can be configured with specific resource requirements (e.g., num_gpus=1).29

 

Managing Workloads with Serve

 

Ray Serve’s power as a control plane is most evident in how it enables developers to manage complex workloads and application structures.

• Model Composition and Deployment Graphs: Ray Serve’s most distinctive feature is its programmable API for model composition. Instead of defining static inference graphs in YAML or another configuration language, developers can compose multiple deployments using standard Python code. A replica of one deployment can obtain a DeploymentHandle to another deployment. This handle acts like a client that can be used to send requests to the target deployment, just like making a regular Python function call. This simple but powerful primitive allows for the creation of arbitrarily complex application graphs. For example, a developer can build a multi-stage RAG pipeline where a “preprocessor” deployment calls a “retriever” deployment, which in turn calls a “generator” deployment. This pattern is also a natural fit for building multi-agent systems, where each agent can be implemented as an independent, stateful, and scalable Ray Serve deployment that communicates with others via deployment handles.6
• Autoscaling: Ray Serve provides built-in, per-deployment autoscaling. The Controller actor periodically collects metrics from each deployment, such as the number of queued and in-flight requests. Based on a user-defined configuration (e.g., target number of in-flight requests per replica), the autoscaler can dynamically increase or decrease the number of replicas for that deployment. This allows each component of a complex application to scale independently based on its specific load, leading to efficient resource utilization.29
• Fault Tolerance: Ray Serve inherits the robust fault tolerance capabilities of Ray Core. If a replica actor fails due to a machine crash or an unhandled exception, the Serve Controller will detect the failure and automatically create a new replica to replace it. If a proxy actor fails, it is also restarted by the Controller. If the Controller actor itself fails, the underlying Ray system will restart it, and it will recover its state from the GCS checkpoint. When running on Kubernetes with the KubeRay operator, this fault tolerance extends to the cluster level, with KubeRay capable of recovering from entire node failures.29

 

Ray Serve for LLMs: The Engine-Agnostic Approach

 

Recognizing the unique demands of LLM serving, the Ray team has developed specialized tooling to simplify these deployments while adhering to Ray Serve’s core philosophy of flexibility and programmability.

• Ray Serve LLM Library: This is a high-level library built on top of Ray Serve that provides abstractions and pre-built patterns for common LLM serving challenges. For example, it includes components for implementing advanced scaling strategies like prefill-decode disaggregation (where the initial prompt processing and subsequent token generation phases are handled by separate, independently scalable groups of replicas) and efficient tensor parallelism across multiple nodes.33
• Engine Integration: A crucial aspect of Ray Serve’s design is that it is engine-agnostic. It is not intended to compete with specialized inference engines like vLLM or TGI on raw kernel performance. Instead, it is designed to orchestrate them. Ray Serve has first-class integrations that allow developers to easily wrap a high-performance backend within a Ray Serve deployment. For example, the vLLM integration allows a replica to instantiate a vLLM engine and use it to process requests. This creates the powerful “hybrid pattern”: Ray Serve provides the flexible, Python-native control plane for application logic, routing, and composition, while vLLM provides the state-of-the-art data plane for inference computation. This architecture combines the best of both worlds, allowing developers to build complex, production-grade LLM applications without sacrificing performance.27

 

Comparative Analysis: Scheduling Philosophies and Performance Trade-offs

 

The architectural deep dives into Ray and TGI reveal two systems built with fundamentally different goals, resulting in distinct scheduling philosophies, scalability models, and performance characteristics. This section provides a direct comparative analysis to synthesize these differences and clarify their respective strengths and weaknesses in the context of distributed scheduling for AI workloads.

 

Control Plane vs. Inference Engine

 

The most critical distinction lies in their primary architectural role, which is a direct consequence of their scheduling philosophies.

• Ray Serve is a programmable control plane. Its scheduling system, inherited from Ray Core, is resource-centric. It is designed to manage the allocation of heterogeneous resources (CPUs, GPUs, memory) to a diverse set of stateful (actors) and stateless (tasks) computations across a multi-node cluster. Its primary goal is to provide a flexible and general framework for building and orchestrating the components of a distributed application. The “bottom-up” scheduler, with its emphasis on data locality and configurable placement strategies, is optimized for this generality.
• Hugging Face TGI is a specialized inference engine. Its scheduling system is request-centric. It is not concerned with general resource allocation but is singularly focused on one metric: maximizing the throughput of LLM inference requests on a given set of GPUs. Its entire scheduling logic, centered around continuous batching, is designed to keep the GPU’s computational units saturated with work by dynamically managing a queue of incoming prompts. It is a highly optimized but inflexible system designed to solve one problem exceptionally well.

 

Scalability and Fault Tolerance

 

These differing paradigms lead to different models for scalability and fault tolerance.

• Ray is natively designed for multi-node scalability. The entire system, from the distributed scheduler to the object store, is built to operate across a large, potentially elastic cluster of machines. Its fault tolerance is a core design principle of the underlying system. Ray Core provides mechanisms to automatically recover from process failures, and when managed by the KubeRay operator on Kubernetes, it can gracefully handle the failure and replacement of entire nodes, with the GCS providing the necessary state for recovery. This makes Ray a robust foundation for building highly available, mission-critical applications.12
• TGI‘s primary scalability model is single-node, multi-GPU via tensor parallelism. It is designed to efficiently utilize all the GPUs on a single powerful machine to serve a very large model. While it is possible to run multiple TGI instances on different nodes behind a load balancer, it is not a natively distributed system in the same way as Ray. Tightly coupled, multi-node tensor parallelism is generally not supported or recommended due to the prohibitive network latency between machines, which would severely degrade performance.35 TGI’s fault tolerance model is also simpler; it relies on its container orchestrator (e.g., Kubernetes) to provide fault tolerance at the process level. If the TGI container fails, Kubernetes will restart it. It does not have its own built-in mechanisms for cross-node state recovery or graceful failover.

 

Performance Profile Synthesis

 

Performance benchmarks confirm the trade-offs inherent in these two designs. The choice of framework has significant implications for throughput and latency, depending on the specific workload.

• Finding 1: Specialization Trumps Generality for Pure Inference: For the specific task of LLM inference, specialized engines that implement advanced techniques like continuous batching and PagedAttention consistently and significantly outperform general-purpose frameworks that use more naive batching strategies. This is the primary reason for the emergence of TGI and vLLM. Benchmarks show that both TGI and a Ray Serve implementation of continuous batching can achieve an approximately 8x throughput improvement over a baseline using static batching, demonstrating the critical importance of this optimization.25
• Finding 2: Performance Among Specialized Engines is Nuanced: The performance gap between leading inference engines like TGI and vLLM is highly context-dependent and subject to rapid change as new optimizations are developed. One benchmark conducted on an NVIDIA A100 GPU found that TGI could process 40 requests per second (RPS) for a Llama-7B model, while vLLM handled 20 RPS under the same conditions.27 Conversely, other analyses suggest TGI may have slightly higher latency than vLLM under heavy loads.36 More recently, Hugging Face has claimed that new optimizations in TGI, particularly around prefix caching and custom kernels, have resulted in a 13x speedup over vLLM for workloads with very long prompts.37 This illustrates that the “fastest” engine is a moving target and depends heavily on the specific model, hardware, and workload characteristics.
• Finding 3: The Control Plane Overhead is Minimal: When Ray Serve is used in the hybrid pattern as a control plane to orchestrate a high-performance backend like vLLM, the overall performance is comparable to that of the standalone backend. Benchmarks show that Ray with vLLM support could handle 10 RPS in a specific test, a figure comparable to the performance of other standalone servers under the same sustained load.27 This indicates that the overhead introduced by Ray Serve’s routing and orchestration layer is minimal, making the hybrid architecture a viable and powerful pattern for production.

The following table synthesizes these architectural and performance characteristics into a concise comparison, providing a high-density reference for technical decision-making.

Dimension	Ray (with Ray Serve)	Hugging Face Text Generation Inference (TGI)
Primary Paradigm	General-purpose distributed compute framework; a control plane for orchestrating complex applications.	Specialized, high-performance LLM inference server; an optimized inference engine.
Scheduling Philosophy	Resource-centric: “Bottom-up” distributed scheduling; resource-aware, locality-aware, and highly configurable for diverse workloads.	Request-centric: Optimized for maximizing GPU utilization via continuous batching of inference requests.
Core Use Case	Multi-model composition, complex pipelines (e.g., RAG), multi-agent systems, A/B testing, and orchestrating other inference servers.	High-throughput, low-latency serving of a single or multiple fine-tuned (LoRA) LLMs.
Flexibility	Extremely high: Arbitrary Python code can be scaled. Engine-agnostic, allowing integration with any backend (vLLM, TGI, etc.).	Moderate: Highly optimized for its specific task. Less flexible for custom pre/post-processing logic outside the model.
Scalability Model	Multi-node native: Designed to scale elastically across a large cluster of nodes.	Single-node, multi-GPU primary: Scales via tensor parallelism on a single machine. Multi-node is not a primary design goal.
Fault Tolerance	System-level: Robust, cluster-wide fault tolerance for actors, tasks, and nodes, especially when managed by KubeRay.	Process-level: Relies on an external orchestrator (e.g., Kubernetes) to restart the container/pod upon failure.
Performance Profile	Depends on the chosen backend engine. When orchestrating vLLM, performance is comparable to standalone vLLM.	State-of-the-art throughput and latency for LLM inference due to continuous batching and kernel optimizations.
Ease of Use	Steeper learning curve: Requires understanding of distributed concepts (actors, tasks, GCS).	Simpler for target use case: Can be run with a single Docker command for a standard model from Hugging Face Hub.

 

Production Deployment and Orchestration

 

Deploying and managing distributed AI applications in production requires robust orchestration, and Kubernetes has emerged as the de facto standard for this task. Both Ray and TGI are designed to run on Kubernetes, but their deployment methodologies and the level of integration with the Kubernetes ecosystem differ significantly, reflecting their core architectural philosophies.

 

Deploying Ray on Kubernetes with KubeRay

 

The official and recommended method for deploying Ray on Kubernetes is through the KubeRay operator.38 An operator is a software extension to Kubernetes that uses custom resources to manage applications and their components. KubeRay provides a Kubernetes-native way to manage the entire lifecycle of Ray clusters, abstracting away much of the underlying complexity.

• KubeRay Operator: The operator is a controller that runs within the Kubernetes cluster. It continuously watches for custom resources related to Ray and takes action to ensure that the actual state of the cluster matches the desired state defined in these resources.41
• Custom Resource Definitions (CRDs): KubeRay introduces three key CRDs that allow users to manage Ray applications as first-class Kubernetes objects 41:

• RayCluster: This CRD defines the configuration of a Ray cluster, including the specifications for the head node pod and one or more groups of worker node pods. It manages the cluster’s lifecycle, including creation, deletion, and, crucially, autoscaling. The KubeRay operator can automatically scale the number of worker pods up or down based on the workload’s resource demands.
• RayJob: This CRD is designed for batch processing workloads. It defines a job to be run on a Ray cluster. When a RayJob is submitted, the KubeRay operator will automatically create a RayCluster according to the specified configuration, submit the job to the cluster once it is ready, and can be configured to automatically tear down the cluster after the job completes, optimizing resource usage.
• RayService: This CRD is specifically designed for serving workloads with Ray Serve. It is composed of two parts: a RayCluster specification and a Ray Serve deployment configuration. RayService provides essential production features like zero-downtime upgrades. When an update is applied, the operator can orchestrate a rolling update, bringing up the new version of the application on a new Ray cluster before tearing down the old one, ensuring continuous availability.

Using KubeRay provides a deeply integrated, robust, and scalable way to run Ray on Kubernetes, handling fault tolerance and lifecycle management in a manner that is idiomatic to the platform.

 

Deploying TGI on Kubernetes

 

Deploying TGI on Kubernetes is a more conventional process, as TGI is distributed as a standard Docker container. It can be deployed using standard Kubernetes resources like Deployment and Service manifests.43

• As a Containerized Service: A typical TGI deployment involves creating a Kubernetes Deployment that specifies the TGI container image, the model to be served (passed as a command-line argument), and necessary environment variables (such as a Hugging Face Hub token). A Kubernetes Service is then created to expose the TGI deployment’s port, providing a stable endpoint for ingress traffic.
• Best Practices for Production:

• Resource Management: The Deployment manifest must include resource requests and limits, specifically for GPUs (nvidia.com/gpu: ‘1’), to ensure that Kubernetes schedules the TGI pod on a node with the required hardware.
• Model Storage: Model weights, which can be tens or hundreds of gigabytes, should not be baked into the container image. A common best practice is to use an initContainer. This is a secondary container that runs to completion before the main TGI container starts. The initContainer’s role is to download the model weights from a repository (like Hugging Face Hub or a cloud storage bucket) into a shared volume (typically an emptyDir volume) that is then mounted by the main TGI container. This decouples the application image from the model artifacts, making updates more efficient.46
• High Availability: To achieve high availability and load balancing, the replicas field in the Deployment manifest can be set to a value greater than one. Kubernetes will then ensure that multiple TGI pods are running, and the Service will automatically load-balance incoming requests across them.

 

The Hybrid Pattern: Ray Serve Orchestrating TGI/vLLM

 

The most sophisticated and powerful production pattern combines the strengths of both frameworks. In this architecture, Ray Serve acts as a flexible control plane to orchestrate one or more high-performance TGI or vLLM backends, which serve as the data plane.8

• Architectural Blueprint:

1. A RayService is deployed to the Kubernetes cluster using the KubeRay operator. This provides the underlying scalable and fault-tolerant Ray cluster.
2. The Ray Serve application, defined in Python, is configured within the RayService. This application typically consists of multiple deployments.
3. An ingress deployment is created to handle all incoming traffic. This deployment can be built using FastAPI integration in Ray Serve and is responsible for tasks like authentication, request validation, and complex business logic (e.g., the “retrieve and rank” steps in a RAG pipeline).
4. The ingress deployment then uses a DeploymentHandle to route the preprocessed request to one or more backend deployments.
5. Each backend deployment is a Ray actor that wraps a high-performance inference engine. For example, a backend replica could be responsible for running a vLLM server process internally or, more efficiently, could directly use the vLLM Python library to perform inference.

• Benefits of the Hybrid Pattern: This architecture provides the best of both worlds. It leverages Ray Serve’s programmable, Python-native control plane to build and manage complex application logic, composition, and routing. Simultaneously, it offloads the performance-critical inference computation to a specialized, state-of-the-art engine like vLLM or TGI. This pattern allows for the independent scaling of the application logic layer and the inference layer, provides a unified entry point for complex services, and enables developers to build sophisticated AI applications without compromising on raw inference throughput.

 

Decision Framework and Strategic Recommendations

 

The choice between Ray and Hugging Face TGI is not a binary decision but an architectural one that depends on the complexity of the application, performance requirements, and the desired level of operational control. This final section provides a clear decision framework to guide architects and engineers in selecting the right tool, or combination of tools, for their specific needs.

 

Use Case Decision Matrix

 

The following matrix outlines the ideal scenarios for each framework, providing actionable guidance for common AI application patterns.

• Choose Hugging Face TGI (Standalone) When:

• Primary Use Case: The application is a simple, high-throughput API endpoint for a single LLM or a set of fine-tuned LoRA adapters on a common base model.
• Application Logic: Pre- and post-processing logic is minimal and can be handled client-side or in a separate microservice that calls the TGI endpoint.
• Operational Goals: Simplicity and speed of deployment are paramount. The goal is an out-of-the-box, enterprise-supported solution that is tightly integrated with the Hugging Face model ecosystem.
• Example Scenario: Building a straightforward chatbot backend where the primary task is to forward user messages to an LLM and stream the response back. For this use case, TGI provides a balanced combination of ease of use and high performance.8

• Choose Ray Serve (with a High-Performance Backend like vLLM/TGI) When:

• Primary Use Case: The application involves a complex, multi-stage pipeline that requires the composition of multiple models or business logic steps.
• Application Logic: The service requires significant server-side pre- or post-processing, integration with external systems like vector databases, or the execution of custom Python business logic within the request-response path.
• Operational Goals: The platform needs to support advanced deployment strategies like dynamic routing for A/B testing or canary deployments. The system must be ableto orchestrate multiple, independently scalable components.
• Example Scenarios:

• Retrieval-Augmented Generation (RAG): A complex pipeline involving a retriever model, a reranker model, and a generator LLM, all composed and managed within a single Ray Serve application.8
• Multi-Agent Systems: An application where multiple autonomous agents, each implemented as a stateful Ray Serve deployment, need to communicate and collaborate to solve a problem.6
• Multi-Model Endpoints: A service that needs to serve a diverse portfolio of models (e.g., computer vision, ASR, and LLMs) through a unified API, with Ray Serve acting as the central routing and orchestration layer.8

 

Future Outlook: The Convergence of Control and Compute

 

The AI infrastructure landscape is in a state of rapid evolution, and the distinct lines between frameworks like Ray and TGI are beginning to blur in strategic ways. This convergence points towards a future where a standardized, high-performance data plane is managed by a flexible, programmable control plane.

• TGI’s Evolution: TGI is evolving from a monolithic implementation into a modular system with a pluggable backend architecture.28 This indicates a strategic move to position TGI as a stable, feature-rich gateway or frontend that can leverage the best-performing inference kernel (be it vLLM, TensorRT-LLM, or its own native implementation) for any given hardware and model combination.
• Ray’s Evolution: Simultaneously, Ray is building more specialized, high-performance libraries and abstractions specifically for common, demanding workloads like LLM serving. The Ray Serve LLM library, with its built-in patterns for tensor parallelism and advanced scaling strategies, is a prime example of this trend.33 Ray is moving up the stack from a general-purpose engine to providing more opinionated, high-level solutions.

The ultimate trajectory suggests a future where the “inference engine” becomes a commoditized, pluggable component in the AI data plane. The key differentiator and source of value will shift to the control plane—the orchestration framework that provides the best developer experience, the most flexible programming model, the most robust operational tooling, and the greatest ability to compose these powerful engines into sophisticated, end-to-end applications. In this future, Ray is exceptionally well-positioned to become the leading open-source standard for the programmable AI control plane. The critical decision for architects will therefore be less about which specific inference engine to choose at any given moment, and more about which orchestration framework provides the most powerful and extensible foundation for building the next generation of AI-powered systems.