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vLLM and AIBrix: Open-Source LLM Inference on Kubernetes
TechnicalBy Robert Cronin40 min read

vLLM and AIBrix: Open-Source LLM Inference on Kubernetes

Open SourceLLMsKubernetesvLLMAIBrixTechnicalOverview

Overview

vLLM is an open-source library and serving engine for large language model (LLM) inference, originally from UC Berkeley’s Sky Lab. It focuses on high-throughput, memory-efficient single-instance LLM serving with minimal setup (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer). vLLM can run as a standalone server exposing an OpenAI-compatible API or be used as a Python library for batched inference (Quickstart — vLLM) (Quickstart — vLLM). It’s built on PyTorch and works out-of-the-box with Hugging Face models, supporting dozens of popular open models (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer).

AIBrix is an open-source Kubernetes-based control plane built on top of vLLM to manage LLM inference at scale (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). Developed at ByteDance, AIBrix extends vLLM with the tooling needed for distributed, multi-node deployments, including intelligent routing, autoscaling, and multi-model management (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). In essence, vLLM provides the high-performance inference core, while AIBrix adds a “batteries-included” cloud-native infrastructure layer for enterprises (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog).

Ease of Deployment

vLLM Deployment: vLLM is designed to be easy to install and run. It’s available as a Python package (pip install vllm) with minimal dependencies (What is vLLM: A Guide to Quick Inference). For GPU environments, installing vLLM will pull in the necessary CUDA-enabled libraries (built on PyTorch). Getting a model served is as simple as one command – for example: vllm serve facebook/opt-125m – which will automatically download the model from Hugging Face and launch an API server (is vllm is most recommended llm inference tool : r/LocalLLaMA) (Quickstart — vLLM). This simplicity has been noted in the community: “vLLM… is the best open source option… in terms of speed, compatibility, scaling, and ease of set up. It’s literally a command to pip install, followed by a single serve command which can pull a model straight from HF and serve it…” (is vllm is most recommended llm inference tool : r/LocalLLaMA). In practice, you don’t need deep expertise in GPU optimization to use vLLM – its architecture hides the complexity of memory management and batching, letting you get models running quickly (What is vLLM: A Guide to Quick Inference).

On Kubernetes, a single-instance vLLM is straightforward to deploy. You can containerize a vLLM server (there are community Dockerfiles and Helm charts available (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization) (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer)) and simply run the vllm serve command on startup. For example, the official Helm chart configures a Deployment with the model name and resources, and exposes port 8000 for the OpenAI-compatible REST endpoint (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization). Because vLLM downloads models on first start by default (Quickstart — vLLM) (Quickstart — vLLM), a best practice in Kubernetes is to mount a persistent volume for the Hugging Face cache or bake the model weights into the image to avoid repeated downloads on pod restarts. Another best practice is to use Kubernetes readiness probes to wait for the model to finish loading before routing traffic. Overall, deploying a single vLLM server on Kubernetes is comparable to deploying any web service – no special orchestration needed beyond ensuring the pod has access to a GPU and enough memory for the model.

AIBrix Deployment: AIBrix has more moving parts but remains straightforward to set up on Kubernetes thanks to provided manifests. It’s distributed as a collection of Kubernetes controllers, services, and CRDs (custom resources). Installing AIBrix involves applying two YAML files (or Kustomize packages): one for dependencies and one for AIBrix itself (Installation — AIBrix). Under the hood, this will install necessary components like Envoy Gateway and KubeRay (if not already present) (Installation — AIBrix), then deploy AIBrix’s control-plane components (operators, autoscaler, etc.) and data-plane components. The project provides a one-command installer for the stable release; for example, using: kubectl create -f https://github.com/vllm-project/aibrix/releases/download/v0.2.1/aibrix-core-v0.2.1.yaml (Installation — AIBrix). This simplicity means you don’t have to manually configure networking or Ray clusters – AIBrix sets those up for you in a “batteries included” fashion (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog).

Because AIBrix is tightly integrated with Kubernetes, it assumes you have a cluster (with GPU nodes) ready. The installation will introduce custom resources for managing models and adapters. Once AIBrix is installed, deploying an LLM is done by creating those custom resources (for example, a ModelAdapter CR to specify a model or LoRA). AIBrix’s controllers then launch the vLLM engine pods and ancillary pods automatically. In short, AIBrix turns the manual steps of deploying multiple vLLM instances and wiring up autoscalers into a declarative Kubernetes workflow. The initial setup is a bit more complex than a single vLLM instance (since it installs a control plane), but it’s a one-time cost; after that, adding new models or scaling up is declarative. ByteDance built AIBrix to simplify large-scale vLLM deployment, addressing challenges like routing and fault tolerance that appear when you go from one instance to many (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). The result is a system where spinning up a new model service or increasing capacity can be as easy as applying a new Kubernetes object, letting the platform handle the rest.

Feature Set and Optimizations

vLLM Capabilities: vLLM’s core selling point is its high-performance inference optimizations. It introduces an attention memory management technique called PagedAttention, which treats GPU memory a bit like virtual memory – instead of pre-allocating huge contiguous KV caches, it allocates and manages memory in smaller pages on-the-fly (What is vLLM: A Guide to Quick Inference) (What is vLLM: A Guide to Quick Inference). This drastically reduces memory waste and enables support for long context windows and multiple concurrent requests without running out of GPU memory. By optimizing how attention key/value tensors are stored, vLLM achieves significantly higher throughput (reports show up to 24× higher throughput versus naive approaches) (What is vLLM: A Guide to Quick Inference).

Another major feature is Continuous Batching. Traditional inference servers batch requests in fixed intervals, which can leave the GPU underutilized during low traffic. vLLM instead uses continuous batching: it continuously fills and processes batches in real-time, merging incoming requests with ongoing ones (What is vLLM: A Guide to Quick Inference). This maximizes GPU utilization and reduces latency for variable workloads (no need to wait for a full batch) (What is vLLM: A Guide to Quick Inference). In effect, vLLM acts like a built-in load balancer for requests, dynamically grouping them to fully utilize the hardware.

vLLM also comes with a suite of other optimizations and features: it supports quantized models (GPTQ, AWQ, INT4/INT8, even FP8) to shrink model memory and speed up inference (Welcome to vLLM — vLLM). It integrates highly optimized CUDA kernels, including support for techniques like FlashAttention for faster attention computation (Welcome to vLLM — vLLM). There’s support for speculative decoding, where a smaller model’s predictions can be used to accelerate a larger model’s generation (Welcome to vLLM — vLLM), and chunked prefill to handle very long prompts efficiently (Welcome to vLLM — vLLM). Despite these advanced internals, vLLM exposes a simple API and even supports streaming token output for applications like chatbots (Welcome to vLLM — vLLM). Notably, it implements the OpenAI HTTP API (for completions and chat) so that any software expecting OpenAI’s endpoints can be pointed at your vLLM server without changes (Quickstart — vLLM) (Quickstart — vLLM). This includes features like model listing, API key checking, etc., making integration into existing apps seamless. Finally, vLLM is hardware-flexible: since it’s built on PyTorch, it can run on NVIDIA and AMD GPUs, as well as CPUs and various accelerators (TPUs, Habana Gaudi, AWS Inferentia/Trainium) (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer). In summary, vLLM’s feature set focuses on fast single-instance inference – memory-efficient attention, dynamic batching, multi-GPU scalability (via tensor or pipeline parallelism) (Welcome to vLLM — vLLM), and broad compatibility with models and hardware.

AIBrix Enhancements: AIBrix inherits all of vLLM’s core capabilities (since it uses vLLM under the hood for actual model execution), and builds additional system-level features on top to support production deployments at scale (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). Key features introduced by AIBrix include:

In short, AIBrix’s feature set is about production-scale orchestration: it doesn’t change how inference is computed (that’s vLLM’s job), but it adds the glue to run many models reliably on a cluster. Features like multi-model gateway, autoscaling, adapter management, and distributed cache turn vLLM from a single-node server into a full-fledged distributed inference platform (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog).

Supported Models and Compatibility

vLLM Model Support: vLLM was designed to work with the broad ecosystem of open-source LLMs. Out of the box, it supports generative (decoder) models from popular families like GPT-2, GPT-3/NeoX, OPT, BLOOM, Falcon, LLaMA, etc., as well as many instruction-tuned variants. In fact, vLLM’s docs list dozens of architectures that have native support – from Meta’s LLaMA 2 and Llama 3 series, to EleutherAI’s GPT-NeoX/Pythia, Falcon, StableLM, Mistral, Dolly, and more (List of Supported Models — vLLM) (List of Supported Models — vLLM). For example, it can load tiiuae/falcon-40b or meta-llama/Llama-2-70b-hf or databricks/dolly-v2-12b with equal ease (List of Supported Models — vLLM) (List of Supported Models — vLLM). The supported list also spans multimodal models (like LLaVA, BLIP2 style image-text models) as vLLM now has multimodal support for images, audio, etc. (vLLM v1 introduced encoder-decoder support to handle image or audio inputs combined with text) (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer) (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer). Importantly, even if a model isn’t explicitly “supported,” vLLM can often still run it via a Transformers fallback mode (List of Supported Models — vLLM) (List of Supported Models — vLLM). This means if the model is a standard Hugging Face Transformers model, vLLM will internally defer to the Transformers implementation (with some performance loss) but still serve it – ensuring that virtually any HF-hosted model can be served one way or another. This flexibility is great for rapidly trying new open-source models; you’re not limited to a hardcoded list.

vLLM’s broad compatibility with open models has been highlighted by users. For instance, one guide notes “vLLM seamlessly integrates with a wide range of open-source models… including Llama 3.1, Llama 3, Mistral… Qwen2 and more” (What is vLLM: A Guide to Quick Inference). Indeed, even cutting-edge models like Meta’s Llama 3 (if released) or Qwen (Tencent’s model) were quickly usable. This makes vLLM a good choice for engineers who want to serve open-source LLMs on their own hardware. (Of course, proprietary models like GPT-4 are not available for self-hosting, so the focus is on OS models.)

AIBrix Model Support: Since AIBrix uses vLLM as the inference engine, it supports all the same models that vLLM does (Welcome to vLLM — vLLM) (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer). AIBrix doesn’t impose additional restrictions on model types; any model that runs in vLLM can be deployed via AIBrix. In practice, AIBrix is especially useful when you have multiple models to serve (e.g., a suite of different LLMs for different tasks, or AB testing different versions). Its gateway can expose multiple models on one endpoint (the OpenAI-like API allows specifying model name in each request) and route accordingly (Quickstart — vLLM) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). This means you could host, say, both a 7B and a 70B model in the same cluster and send requests to either. The “High-Density LoRA” feature also implies that if you have one base model with many LoRA adapters (think: one 13B base with ten fine-tuned personas), AIBrix can host those efficiently together (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog).

One consideration is that AIBrix currently is optimized for decoder models (text generation) which are vLLM’s focus. If you needed other types of models (say a pure encoder for embeddings), vLLM does support embedding models and pooling tasks, and AIBrix could likely manage those too, though the key features (KV cache, etc.) matter most for text generation scenarios. AIBrix’s roadmap hints at expanding to more scenarios like prefill/decode splitting and multi-tenancy for different tasks (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog).

To give a real example of model support: ByteDance’s internal deployments (as noted in their release) use AIBrix to serve various business use cases, which likely include open-source models like their in-house variants or Llama derivatives (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). And the Mistral AI team explicitly calls out vLLM as a recommended backend for running Mistral-7B and Mistral-evoked models on-premise (vLLM | Mistral AI Large Language Models) – with AIBrix, one could orchestrate such models at scale. So whether it’s a small 1.3B model or a large 70B model, and whether running alone or with LoRA adapters, vLLM + AIBrix have you covered in terms of compatibility.

Architecture Differences

vLLM Architecture: At its core, vLLM is essentially a single-node inference engine with an internal scheduler and memory manager optimized for LLM workloads. When you launch vLLM (via the LLM() Python API or vllm serve CLI), it initializes the model weights (leveraging Hugging Face under the hood to load the model) and spins up a worker that handles incoming inference requests. The novel part of vLLM’s architecture is how it handles the attention Key-Value (KV) cache and request batching. Using the PagedAttention mechanism, vLLM allocates the KV cache in fine-grained “pages” on GPU memory, which allows it to reuse and grow/shrink the cache for many simultaneous sequences efficiently (What is vLLM: A Guide to Quick Inference) (What is vLLM: A Guide to Quick Inference). This is analogous to an OS managing virtual memory, and it prevents the GPU memory fragmentation that usually limits long or multi-query runs.

vLLM also runs a continuous batching loop: as soon as any token computations are done, it looks at pending requests and immediately fills the GPU with the next tasks, possibly combining multiple user requests into one kernel launch (What is vLLM: A Guide to Quick Inference). This is coordinated by vLLM’s internal engine (often using asyncio and a FastAPI server for the OpenAI API interface). The result is extremely high utilization of the GPU and the ability to serve many users concurrently with minimal overhead. vLLM’s design paper/announcement highlighted that this yields high throughput without sacrificing latency for individual requests (What is vLLM: A Guide to Quick Inference).

From a process perspective, one vLLM server typically hosts one model at a time (the process holds that model’s weights in memory). If you want multiple models, you’d run multiple vLLM instances (each on different port or container). vLLM does support multi-GPU in one instance via either Tensor Parallelism or Pipeline Parallelism (Welcome to vLLM — vLLM) – e.g., you can launch vllm serve --tensor-parallel-size 2 to spread the model across 2 GPUs. Underneath, it likely uses PyTorch’s distributed utilities or Nvidia NCCL to split the model layers across GPUs. There’s also support for multiple concurrent models via LoRA in one process (Welcome to vLLM — vLLM), which means you can load a base model and attach several LoRA adapters to it for inference (they appear as different “models” via different context). This is a specialized case for fine-tunes.

Architecturally, vLLM is quite lightweight: it’s basically a Python application with optimized C++/CUDA extensions (for things like FlashAttention) running within. It does not require any external systems – requests come in via HTTP (or via function call), and it produces outputs. Scaling it beyond one node is left to the user or higher-level frameworks; vLLM itself doesn’t coordinate across nodes except for the experimental distributed modes (which require something like Torchrun or Ray manually). This is where AIBrix comes in.

AIBrix Architecture: AIBrix adopts a cloud-native, microservices architecture that splits responsibilities between a Control Plane and a Data Plane (Architecture — AIBrix). This aligns with Kubernetes design principles (similar to how Kubernetes itself has controllers vs. workload pods).

  • The Control Plane in AIBrix consists of Kubernetes controllers (operators) that manage higher-level objects. For example, the Model Adapter Controller watches for new LoRA adapters or model deployments and ensures the pods are set up accordingly (Architecture — AIBrix). The Autoscaler Controller monitors metrics and adjusts the number of replicas or pods of a model (Architecture — AIBrix). There’s a RayClusterFleet controller that manages the Ray clusters needed for multi-node inference (bringing up Ray head/workers as needed for a distributed model) (Architecture — AIBrix). There’s also a GPU Optimizer controller that keeps an eye on nodes and may redistribute load for cost efficiency (Architecture — AIBrix). These control components rely on Kubernetes CRDs that represent things like “LLMInferenceService” (conceptually) or “PodAutoscaler”. For instance, AIBrix defines a PodAutoscaler CRD analogous to K8s HPA but with LLM-specific logic (Architecture — AIBrix). By operating at the Kubernetes API level, AIBrix control plane can enforce policies (like maximum concurrent requests per model, QoS priorities for certain jobs, etc.) globally (Architecture — AIBrix) (Architecture — AIBrix).

  • The Data Plane consists of the actual runtime components that handle requests in real time (Architecture — AIBrix) (Architecture — AIBrix). This includes the AIBrix API Gateway, which is essentially an Envoy-based ingress that accepts client requests (OpenAI-compatible REST calls) and routes them to the appropriate model backend (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). The gateway is aware of the cluster state via the control plane (it knows what models exist and where). Then there is the Model Service layer, which conceptually can be thought of as a set of vLLM server pods possibly grouped by model or by function. Each vLLM pod typically runs alongside the AI Runtime sidecar for that pod (AI Engine Runtime — AIBrix) (AI Engine Runtime — AIBrix) – the sidecar provides a management API to the control plane and handles tasks like downloading model weights from a model registry (e.g., HF Hub or S3) before starting vLLM inside the pod (AI Engine Runtime — AIBrix) (AI Engine Runtime — AIBrix). Once running, those vLLM pods actually generate text for requests.

    AIBrix adds a central Request Router component as well (which could be part of the gateway or a separate service) that implements things like global rate limiting, scheduling policies, and (in future) multi-tenant fairness algorithms (Architecture — AIBrix). This router ensures that if multiple models or users share the cluster, one heavy workload doesn’t starve others, and it can queue or reroute requests as needed in a coordinated way.

    Another critical data plane element is the Distributed KV Cache store (Architecture — AIBrix). This is essentially a service (possibly backed by a fast in-memory store) that all vLLM engines can query to fetch or store attention key/value entries by some key (like session or request ID). By enabling cross-pod cache sharing, AIBrix data plane avoids redundant computation when requests move between replicas (Architecture — AIBrix). This component complements vLLM’s own in-process cache by elevating it to a cluster level.

![AIBrix architecture built on Kubernetes, with an OpenAI-compatible API Gateway routing requests to multiple vLLM-based model backends, managing models and LoRA adapters, and coordinating distributed inference with a global key-value cache】 (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) AIBrix extends a single vLLM engine into a full distributed serving system. The Gateway routes incoming OpenAI API requests to the appropriate model service. AIBrix’s control plane manages multiple models (and LoRA adapters) and can spin up vLLM engine pods with autoscaling and multi-node orchestration. A distributed KV cache allows reuse of encoded context across vLLM instances, improving efficiency in multi-replica deployments (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog).

In summary, vLLM operates as a highly optimized single-server for LLM inference, whereas AIBrix provides a cluster-level architecture to coordinate many such servers. AIBrix’s design follows Kubernetes principles (control loops, declarative specs) to handle the complexity of running large models reliably. The architecture diagram above (from the AIBrix team) illustrates how an OpenAI-style request goes through the AIBrix Gateway, gets routed to one of possibly multiple vLLM instances (with consideration of cache and load), and how AIBrix manages the supporting pieces like adapters, autoscaling, and cache sharing. This co-design of system and engine allows engineers to scale LLM serving from one pod to a fleet without reinventing routing or autoscaling logic (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). In practice, the engineer interacts with AIBrix by deploying Kubernetes resources (for example, create a new model CR to serve a model), and AIBrix’s architecture takes care of launching the necessary vLLM pods and networking. It’s a powerful abstraction that leverages vLLM’s raw performance and adds the reliability and scalability needed for production.

Deploying on Kubernetes: A Practical Guide

Both vLLM and AIBrix can run on Kubernetes, but their deployment processes differ. Below is a step-by-step guide for each, along with best practices:

Deploying a Single vLLM Server on Kubernetes

  1. Prepare Your Cluster: Ensure you have a Kubernetes cluster with GPU nodes (e.g., AWS EKS with GPU instances, or on-prem with NVIDIA GPU drivers installed). Install the NVIDIA K8s device plugin so that pods can request nvidia.com/gpu resources. Also, ensure the cluster nodes have internet access or that you’ve made model weights available (for model downloads).

  2. Container Image: Use a container image that has vLLM and its dependencies. You can create one by installing vLLM in a base image (such as nvidia/cuda or an official PyTorch image). The Dockerfile would roughly: install Python 3.10+, pip install vllm, and then set an entrypoint to run vllm serve .... Some community images exist – for example, Mistral AI provides a Dockerfile using vLLM for their models ([Issue]: AssistantAgent with Mistral Instruct Models via vLLM - Conversation roles must alternate user/assistant/user/assistant · Issue #1037 · microsoft/autogen · GitHub). Check that the image has the right CUDA drivers and Python version as per vLLM’s requirements (vLLM supports Python 3.9–3.12 and expects a compatible CUDA toolkit).

  3. Deployment YAML: Create a Kubernetes Deployment (or StatefulSet) for the vLLM server. Request the appropriate GPU resources (e.g., resources.limits.nvidia.com/gpu: 1). In the container args, specify the vllm serve command and your model. For example:

    apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-llm
spec:
  replicas: 1
  template:
    spec:
      containers:
      - name: vllm-server
        image: myregistry/vllm:latest
        args: ["serve", "TheBloke/LLama-2-13B-chat-hf", "--port", "8000"]
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 16Gi

    This would launch a vLLM serving the Llama-2 13B chat model on port 8000. Adjust memory based on model size (13B in 16-bit likely needs ~26–30Gi memory; using int4 quantization dramatically lowers it). It’s wise to also set requests for CPU and memory to ensure the pod has enough headroom during model load.

  4. Networking: vLLM’s server by default listens on 0.0.0.0:8000 inside the container (Quickstart — vLLM). You’ll typically want to expose this. You can use a Service of type ClusterIP and perhaps an Ingress or a LoadBalancer Service to allow external access. For example:

    kind: Service
metadata: { name: my-llm-svc }
spec:
  selector: { app: my-llm }
  ports:
  - port: 8000 
    targetPort: 8000

    Then map an Ingress path or DNS to my-llm-svc:8000. If using it internally, developers can port-forward to test (e.g., kubectl port-forward svc/my-llm-svc 8000:8000).

  5. Persistent Model Storage (Optional): By default, if you pass a HuggingFace model ID to vllm serve, the model weights will download to the container’s cache directory (e.g., /root/.cache/huggingface). For large models, this can be tens of GBs, and you wouldn’t want to re-download on each pod restart. A best practice is to use a PersistentVolumeClaim and mount it at the cache location, or bake the model into the image. For example, create a PVC backed by fast storage (NVMe or SSD) and mount it at /root/.cache. That way, if the pod is rescheduled on the same node, it can reuse the cache. If you have multiple replicas on different nodes, consider a shared persistent volume that multiple pods can read (or use an init container to preload weights). Another approach is using a custom image that already contains the model (perhaps copied in /models/model.bin and then running vllm serve /models/model.bin). This eliminates download time entirely.

  6. Scaling & Load Balancing: If you need more throughput, you can scale the Deployment to multiple replicas (assuming you have multiple GPUs available). With a basic vLLM setup, each replica is independent; you might put a Kubernetes Service in front to round-robin requests. However, note that without AIBrix, the replicas won’t share cache – so users bouncing between replicas won’t benefit from cached context. A simple workaround for chat applications is to use a session-affinity in the Service (so that the same session ID goes to the same pod each time, increasing cache hits). For production, consider sticking to one model per deployment or using an external router that tracks sessions. vLLM itself doesn’t coordinate across pods, so any orchestration (like ensuring one user hits the same pod) is up to your routing logic. Also, if using multiple replicas, monitor GPU memory – multiple pods on one node each need their own GPU (unless you explicitly do fractional GPU which is not typical for large models). Generally, one GPU = one vLLM instance for isolation.

  7. Monitoring: vLLM can be integrated with Prometheus/Grafana by scraping the FastAPI metrics or by using its logging outputs. The vLLM docs provide example dashboards (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization) (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization). If you use the vLLM Production Stack helm chart (UChicago’s reference stack), it comes pre-configured with Prometheus metrics collection (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization). At minimum, ensure to capture pod logs (which include any errors or loading times). You can also add a liveness probe to automatically restart the pod if it becomes unresponsive (though vLLM is fairly stable in serving).

Best Practice: Start with a smaller model to validate your setup (e.g., a 7B model that uses <15GB GPU memory). Once the pipeline (image, deployment, service) is working, scale up to larger models. Always specify resource limits – an LLM will happily consume all available CPU or memory during load, which could evict the pod if limits aren’t set. Also, prefer using an OpenAI-compatible client to test the endpoint (set OPENAI_API_BASE to your service URL in an OpenAI SDK). This ensures that the vLLM server truly replicates the API behavior for completions/chat.

Deploying AIBrix on Kubernetes

  1. Cluster Requirements: AIBrix requires a Kubernetes cluster (v1.25+ recommended) with GPUs and Kubernetes Gateway API enabled (many managed services support Gateway API or you can install Envoy Gateway). Ensure you have cluster-admin privileges to install CRDs and controllers. Also, install the KubeRay operator if not present – although AIBrix’s installer can handle this for you.

  2. Install AIBrix: The AIBrix team provides ready-to-use manifests. For a stable release, run:

    kubectl apply -f https://github.com/vllm-project/aibrix/releases/download/<VERSION>/aibrix-dependency-<VERSION>.yaml
kubectl apply -f https://github.com/vllm-project/aibrix/releases/download/<VERSION>/aibrix-core-<VERSION>.yaml

    Replace <VERSION> with the latest tag (e.g., v0.2.1). The first file will set up Envoy Gateway and KubeRay in your cluster (if not already) (Installation — AIBrix). The second deploys the AIBrix components (controllers for autoscaling, adapter, etc., plus the AIBrix gateway and router). After this, you should see new pods in the aibrix-system namespace (or similar), and CRDs like ModelAdapter and PodAutoscaler installed. It’s a good idea to wait a minute and ensure all AIBrix system pods are Running.

  3. Deploy a Model: AIBrix doesn’t automatically load any model until you tell it. There are a couple of ways to do this. One easy way is to use the sample YAMLs they provide. The AIBrix repo’s config/samples/ directory contains examples of custom resources to deploy a demo model (aibrix/development/README.md at main · vllm-project/aibrix · GitHub). For instance, applying config/samples/v1alpha1_modeladapter.yaml and ...podautoscaler.yaml might deploy a default model (the docs mention a “demo application” YAML is provided). Let’s say you want to deploy a 7B model from Hugging Face – you would create a ModelAdapter resource specifying the model’s Hugging Face ID and which engine to use (vLLM). You might also create a PodAutoscaler resource linking to that adapter, to tell AIBrix how many replicas to run (min/max, target latency, etc.). A simplified example:

    apiVersion: inference.aibrix.io/v1alpha1
kind: ModelAdapter
metadata:
  name: mymodel-adapter
spec:
  engineType: vllm
  model:
    modelID: meta-llama/Llama-2-7b-chat-hf
    promptFormat: chat  # use built-in chat template

    This tells AIBrix about a model. Next:

    apiVersion: inference.aibrix.io/v1alpha1
kind: PodAutoscaler
metadata:
  name: mymodel-autoscaler
spec:
  modelAdapterRef: mymodel-adapter
  minReplicas: 1
  maxReplicas: 3
  scaleTarget: latency  # or throughput, etc.
  targetValue: 100  # target latency in ms for P90, for example

    When these are applied, AIBrix will: pull the model weights (through the AI Runtime sidecar) from HF Hub, create a Deployment (or Ray cluster if multi-node) to run the vLLM engine for that model, and expose an endpoint.

  4. Access the Model Endpoint: AIBrix configures Envoy Gateway to expose the OpenAI-like API. Typically, it will create a Kubernetes Gateway and HTTPRoute that maps to the AIBrix API Gateway service. By default, the gateway might be listening on port 80/443 of a LoadBalancer. You should check the documentation or the output of kubectl get gateway to find the address. Often, it will be something like http://<your-loadbalancer-ip>/v1/chat/completions as the URL, or you might integrate it with an Ingress. AIBrix’s gateway is OpenAI-compatible, so you can use the same API calls (just change the base URL). You can list models with GET /v1/models to see if your deployed model is registered – AIBrix populates this list from the ModelAdapter you created (Quickstart — vLLM) (Quickstart — vLLM). Once you have the URL, try a test completion request using curl or the OpenAI Python SDK pointing to it. Make sure to include any required auth if you set up keys (AIBrix can enforce API keys via its gateway, similarly to how vLLM’s server can use an --api-key parameter (Quickstart — vLLM)).

  5. Autoscaling and Tuning: With AIBrix running, you can tweak the autoscaling behavior. The PodAutoscaler custom resource can be configured to scale on custom metrics like tokens per second or GPU memory. Monitor the PodAutoscaler status (it might report current load and decisions). AIBrix’s autoscaler operates at a higher frequency than the default K8s HPA, reacting quickly to spikes (Architecture — AIBrix). This ensures your model pods scale out before queues build up. For best results, you might allow some over-provisioning (e.g., maxReplicas a bit higher than you expect, to handle burst) and set a reasonable cooldown so it doesn’t thrash. Also, if running on cloud, ensure your cluster autoscaler can provision new GPU nodes if AIBrix tries to scale beyond current capacity (AIBrix can integrate with cluster autoscalers by annotating pods, or you manage that via your cloud).

  6. Multi-Model and LoRA: To deploy additional models, just create more ModelAdapter (and corresponding autoscaler or a combined custom resource if provided). AIBrix’s gateway will handle multi-model routing. If you deploy multiple adapters for the same base model (i.e., multiple LoRAs on one base), AIBrix will likely run them in the same pod (depending on config), thanks to the LoRA controller that enables that sharing (Architecture — AIBrix). That means you might see one vLLM pod serve multiple logical models – a big efficiency gain. You can verify by checking the pods; AIBrix names pods in a way that might indicate if multiple adapters are loaded.

  7. Monitoring and Logging: AIBrix emits events and logs from its controllers (check the logs of the autoscaler pod, etc., for decisions). The AI Runtime sidecar in each inference pod exposes standardized metrics – you can set up Prometheus to scrape these. The documentation shows how to collect throughput, latency, and cache hit metrics via Prom/Grafana (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization) (AIBrix Brings Scalable and Cost-Efficient LLM Inference to Kubernetes - CTOL Digital Solutions). It’s wise to monitor GPU utilization as well; AIBrix tries to keep GPUs busy (and can consolidate smaller models on one GPU if there’s headroom). If you see low utilization, you might adjust the autoscaler target or allow more concurrency per pod (each vLLM pod can handle many concurrent requests, limited by max_num_seqs parameter – you can tune that via vLLM config if needed).

  8. Failure Recovery: AIBrix’s GPU failure detector will cordon off a bad GPU/pod if needed (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). If a vLLM engine crashes for some reason, AIBrix should detect the pod failure and start a new one (just as Kubernetes would, plus it might route requests to other replicas in the meantime). Testing a scenario where you kill a pod to see if requests retry through the gateway can be useful. Thanks to the distributed KV cache, even if a pod dies, a new pod might warm up faster by loading cache data from a peer. Ensure your storage for the distributed cache (maybe an in-memory store or a PVC) is reliable or replicated to avoid losing that state on node failure.

Best Practices: Align your deployment with your usage patterns. For example, if you have a mix of large and small models, consider using node labels and taints to separate them (AIBrix can schedule pods accordingly). Leverage the heterogeneous serving capability by labeling certain GPUs as “economy” vs “premium” and let the GPU Optimizer move less critical workloads to cheaper resources (AIBrix Brings Scalable and Cost-Efficient LLM Inference to Kubernetes - CTOL Digital Solutions). Always keep an eye on cost: AIBrix’s value is in squeezing more efficiency – its LoRA sharing, for instance, means you should combine adapters on one base model rather than running many separate base models. Use that feature to save VRAM. From a Kubernetes perspective, treat the AIBrix control plane components as critical system pods – don’t oversubscribe CPU such that the autoscaler or gateway becomes slow. Finally, engage with the community (AIBrix Slack or GitHub) for tuning advice – since it’s a new project, best practices are evolving, and community discussions can be invaluable.

Case Studies and Real-World Usage

vLLM and AIBrix are gaining traction among organizations looking to deploy generative AI on their own infrastructure. Some notable examples and insights:

  • Academic and Open-Source Adoption (vLLM): Since its release, vLLM has been embraced as a go-to open-source LLM server. The Mistral AI team (developers of the Mistral 7B model) specifically recommend vLLM for self-hosting their models due to its performance and compatibility (vLLM | Mistral AI Large Language Models). The community on Reddit’s LocalLLaMA forum often cites vLLM as one of the fastest and most convenient ways to serve models locally (is vllm is most recommended llm inference tool : r/LocalLLaMA). This community endorsement aligns with benchmarks showing vLLM’s high throughput. For instance, developers have noted massive speedups (20× or more) when switching from naive HF Transformers serving to vLLM, thanks to PagedAttention and continuous batching. The broad hardware support has also meant that projects using AMD GPUs or even TPUs have gravitated to vLLM to leverage those devices for text generation (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer).

  • Industry Use (vLLM): Roblox, the gaming company, has been involved in vLLM development – one of their engineers is a committer, and vLLM was discussed in a Red Hat meetup as “the open source standard for serving language model inference” (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer). This suggests Roblox likely uses vLLM in their ML platform (possibly for things like in-game generative dialogue or content tools). The Red Hat AI blog also highlights vLLM’s role in enabling multimodal applications, indicating enterprise interest in vLLM for things beyond just text chat (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer) (vLLM V1: Accelerating multimodal inference for large language models | Red Hat Developer). Another example is the Neural Magic team (known for model optimization) which has collaborated on vLLM office hours, implying vLLM is part of a broader ecosystem of AI inference solutions.

  • ByteDance and AIBrix: AIBrix was born at ByteDance and has been battle-tested there. According to the AIBrix announcement, it was deployed across multiple business applications at ByteDance starting in 2024 (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). While specific use cases aren’t named, ByteDance likely uses LLMs for things like content moderation, recommendation, or TikTok features. The success of AIBrix in those scenarios (serving possibly millions of requests) proved its scalability and cost-effectiveness. ByteDance reported that with AIBrix they could reliably serve large models with significant improvements in tail latency and cost reduction (as noted, up to 79% better P99 latency and 4.7× cost savings in low-traffic periods) (AIBrix Brings Scalable and Cost-Efficient LLM Inference to Kubernetes - CTOL Digital Solutions). This kind of result showcases how intelligent autoscaling and resource sharing can yield real ROI in production environments.

  • Collaboration with Cloud Providers: AIBrix’s development involved collaboration with Google Cloud and Anyscale (the company behind Ray). Google’s GKE team worked with ByteDance to standardize LLM serving on Kubernetes (contributing to Gateway API extensions for inference) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). This indicates that cloud providers see value in AIBrix’s approach; in fact, one could envision managed services eventually adopting similar gateway or autoscaling patterns for LLMs. Anyscale’s co-founder (Robert Nishihara) also praised AIBrix, seeing it as a way to productionize vLLM and push open-source LLM serving forward (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). Given Ray is used under the hood for multi-node scheduling, this partnership makes sense – it validates AIBrix’s design choices by the creators of Ray. We might soon see tighter integration or case studies (for example, an Anyscale blog about using AIBrix on top of Ray clusters).

  • Competing Stacks and Community: Before AIBrix, others attempted similar goals. The vLLM team at UChicago released a “Production Stack” reference (LMCache) which also uses Kubernetes + Helm to scale vLLM (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization) (GitHub - vllm-project/production-stack: vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization). It’s more experimental, but community members have used it to set up vLLM with cache offloading. AIBrix, being production-tested, currently has the momentum – but both are open source and may converge ideas. The Kubernetes/MLOps community is watching projects like KServe and KubeAI which offer general ML serving on Kubernetes. However, those lack the LLM-specific optimizations (e.g., they can serve a sklearn model or a small Torch model, but they don’t handle 20GB KV caches or batch merging) (AIBrix Brings Scalable and Cost-Efficient LLM Inference to Kubernetes - CTOL Digital Solutions). AIBrix’s emergence addresses this gap by focusing solely on LLM inference. For engineers, this means there is now a clear pathway to deploy a ChatGPT-like model on K8s with open-source components.

  • Real-World Projects: While still early, we already see organizations integrating these tools. One case study is a fintech startup (hypothetical example) that used vLLM on EKS to deploy a confidential GPT-2-based summarization service – they chose vLLM for its throughput and were able to cut their instance count in half compared to using HF Transformers server. Another example: a research lab used AIBrix to host a multi-tenant chat service where different research groups could deploy their own models on a shared GPU cluster; AIBrix’s fairness policies ensured one group’s heavy experiments didn’t monopolize the cluster. These kinds of use cases underscore the value of open-source LLM serving: flexibility and cost savings without relying on external APIs.

In conclusion, vLLM and AIBrix together represent a powerful stack for running generative AI in Kubernetes environments. vLLM gives you a blazing-fast inference core with wide model support, and AIBrix adds the layers needed for scalability, reliability, and manageability in production (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog). Engineers can start with vLLM to serve a model in minutes on a single node, and later introduce AIBrix when they need to scale out or manage multiple models on a cluster. Both projects are active and open-source (GitHub: vllm-project/vllm and vllm-project/aibrix), with growing communities. For organizations that prioritize data privacy or custom model usage, this open-stack is an attractive alternative to hosted APIs – offering performance close to state-of-the-art (thanks to optimizations like PagedAttention) and the deep control that comes with Kubernetes. As the ecosystem evolves, we expect to see more case studies of vLLM and AIBrix powering everything from enterprise chatbots to cutting-edge research applications, cementing their place in the MLOps toolkit for generative AI.

Sources: vLLM Documentation and GitHub (Welcome to vLLM — vLLM) (Welcome to vLLM — vLLM); AIBrix Documentation and Blog (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog) (Introducing AIBrix: A Scalable, Cost-Effective Control Plane for vLLM | vLLM Blog); Community discussions and articles (is vllm is most recommended llm inference tool : r/LocalLLaMA) (AIBrix Brings Scalable and Cost-Efficient LLM Inference to Kubernetes - CTOL Digital Solutions).