Scaling GenAI: Orchestrating High-Throughput Inference with vLLM and Ray Serve

We have all seen the demos. A developer spins up a Llama 3 or Mistral model on a local notebook, types in a prompt, and watches the magic happen. It is impressive, it is functional, and it is completely unsuited for production.

The transition from a proof-of-concept (PoC) to a production-grade Generative AI application is where the real engineering challenges begin. When you move from one user to ten thousand, the economics of GPU compute and the physics of latency collide. IT professionals and CTOs are quickly realizing that the standard Hugging Face transformers pipeline is not enough to handle high-concurrency traffic without burning a hole in the cloud budget.

To solve the "Day 2" operations problem of Generative AI, we need a new stack. We need memory efficiency that squeezes every drop of performance out of NVIDIA A100s or H100s, and we need an orchestration layer that scales dynamically with demand. Enter the power couple of modern AI infrastructure: vLLM and Ray Serve. In this post, we will explore how combining these technologies allows Nohatek to help clients build inference engines that are not just fast, but economically viable at scale.

The Bottleneck: Why Standard Inference Fails at Scale

Before we discuss the solution, we must understand the problem. Large Language Models (LLMs) are memory-bound. During inference, the Key-Value (KV) cache—which stores the context of the conversation—grows dynamically. In traditional serving approaches, memory is allocated in contiguous blocks based on the maximum possible sequence length. This leads to massive fragmentation and wasted GPU memory (VRAM).

Result? You might have a 80GB A100 GPU, but you can only serve a handful of concurrent requests because the memory is "reserved" but empty. This creates a queue, driving up latency and destroying the user experience.

The vLLM Solution: PagedAttention
Inspired by virtual memory management in operating systems, vLLM introduces PagedAttention. It breaks the KV cache into non-contiguous blocks, allowing the system to fill the GPU memory almost completely without fragmentation.

Furthermore, vLLM utilizes Continuous Batching. In traditional batching, the GPU waits for all requests in a batch to finish before starting the next one. If one request generates 50 tokens and another generates 500, the GPU sits idle waiting for the long one to finish. vLLM processes requests at the token level, inserting new requests immediately as others finish. This results in up to 24x higher throughput compared to standard Hugging Face implementations.

The Orchestrator: Scaling Out with Ray Serve

vLLM makes a single GPU efficient, but how do you manage a fleet of them? How do you handle autoscaling, load balancing, and model composition? This is where Ray Serve enters the architecture. Ray is a unified framework for scaling AI and Python applications, and Ray Serve is its model-serving library specifically designed for the complexities of Python-based ML.

Ray Serve provides three critical capabilities for GenAI production:

• Granular Autoscaling: Ray monitors the queue of incoming inference requests. As traffic spikes, Ray can dynamically provision new replicas (actors) across your cluster. When traffic drops, it scales down to zero to save costs.
• Model Composition: Real-world AI apps rarely rely on a single model. You might need a guardrail model (to check for PII), a router model, and the main LLM. Ray Serve allows you to chain these distinct deployments together in a Python-native graph.
• Hardware Decoupling: Ray abstracts the infrastructure. You define the resource requirements (e.g., num_gpus=1), and Ray schedules the actor on the appropriate node, whether it is on AWS, Azure, or an on-premise cluster managed by Nohatek.

By wrapping the vLLM engine inside a Ray Serve deployment, we create a service that is both computationally dense (thanks to vLLM) and horizontally scalable (thanks to Ray).

Blueprint: Implementing the vLLM and Ray Stack

For developers and architects looking to implement this, the integration is surprisingly elegant. Instead of managing complex HTTP servers manually, we use Ray's decorators to define our deployment logic. Below is a simplified architectural pattern we often implement for high-throughput clients.

The core logic involves initializing the AsyncLLMEngine from vLLM within the __init__ method of a Ray Serve deployment class. This ensures the model weights are loaded once when the replica starts, rather than per request.

import ray
from ray import serve
from vllm import AsyncLLMEngine, EngineArgs, SamplingParams

@serve.deployment(ray_actor_options={"num_gpus": 1})
class VLLMDeployment:
    def __init__(self):
        # Initialize vLLM engine with PagedAttention
        args = EngineArgs(model="meta-llama/Llama-3-70b-hf")
        self.engine = AsyncLLMEngine.from_engine_args(args)

    async def __call__(self, request):
        # specific request handling logic here
        prompt = await request.json()
        results = await self.engine.generate(prompt, SamplingParams(...))
        return results

# Deploy the service
app = VLLMDeployment.bind()

In a production environment, this code runs inside a Docker container orchestrated by Kubernetes (KubeRay). The architecture allows us to expose a standard OpenAI-compatible API endpoint, meaning your frontend applications do not need to change—only the backend engine gets a massive turbo boost.

Key Configuration Tips:

• Tensor Parallelism: For models larger than a single GPU (like Llama-3-70B), use Ray to manage tensor parallelism, splitting the model across multiple cards automatically.
• Request Batching: While vLLM handles continuous batching, configuring Ray's max_concurrent_queries ensures you do not overload the engine before it can process the queue.

The Business Case: Cost, Latency, and TTFT

Why should a CTO care about this specific stack? It comes down to the metrics that drive business value: Time to First Token (TTFT) and Cost Per Token.

In interactive applications (chatbots, copilots), TTFT is the most critical metric. It is the time between the user hitting "Enter" and seeing the first word appear. High latency here feels like lag. By optimizing memory access, vLLM minimizes TTFT even under load.

From a cost perspective, the math is simple. If a standard deployment can handle 10 concurrent users on an A100 GPU, and a vLLM+Ray deployment can handle 100 users on the same hardware, you have effectively reduced your infrastructure bill by 90%. In the era of expensive cloud compute, this optimization is not just a technical detail—it is a competitive advantage.

At Nohatek, we have observed that moving clients from standard containerized deployments to a Ray-orchestrated vLLM cluster often results in a 2x-4x reduction in total cloud spend while improving reliability. It turns GenAI from a cost center into a scalable asset.

Scaling Generative AI is no longer about just picking the smartest model; it is about engineering the most efficient pipeline. The combination of vLLM's memory optimization and Ray Serve's distributed orchestration provides a robust foundation for enterprise-grade AI.

However, configuring these systems for optimal tensor parallelism, setting up proper autoscaling rules, and securing the endpoints requires deep expertise. If your organization is looking to move beyond the prototype phase and deploy high-performance AI infrastructure, Nohatek is here to guide you.

Ready to scale your AI production? Contact our team today to discuss your architecture.