Building Scalable LLM Infrastructure with Docker Swarm and Ray

Large Language Models (LLMs) have revolutionized how we approach AI applications, but deploying them at scale presents unique challenges. In this post, I'll share my experience building a production-ready LLM infrastructure that achieved a 44% reduction in Time To First Token (TTFT).

The Challenge

When working on conversational AI platforms, we faced several critical issues:

High latency in model inference
Inefficient resource utilization across GPU clusters
Difficulty scaling across multiple GPUs
Complex deployment processes that slowed development

Our Solution: Docker Swarm + Ray + vLLM

We architected a solution combining three powerful technologies:

1. Docker Swarm for Orchestration

Docker Swarm provided us with simple yet effective container orchestration. Unlike Kubernetes, Swarm offered:

Simplified cluster management
Built-in load balancing
Easy service discovery
Minimal operational overhead

# Initialize Docker Swarm
docker swarm init --advertise-addr <manager-ip>

# Deploy LLM service stack
docker stack deploy -c docker-compose.yml llm-stack

2. Ray for Distributed Computing

Ray's distributed computing capabilities allowed us to:

Parallelize model inference across multiple GPUs
Implement efficient batching for improved throughput
Handle dynamic scaling based on demand
Manage resource allocation intelligently

import ray
from ray import serve

@serve.deployment(num_replicas=4, ray_actor_options={"num_gpus": 1})
class LLMService:
    def __init__(self):
        self.model = load_model()
    
    async def __call__(self, request):
        return await self.model.generate(request.prompt)