vLLM: Deploying LLMs at Scale

A comprehensive guide on high-performance LLM inference

As organizations race to integrate Large Language Models (LLMs) into products and workflows, they quickly encounter one central challenge: scaling inference efficiently. While training requires enormous GPU clusters, serving LLMs reliably, cheaply, and with low latency poses an equally complex engineering problem. Traditional inference engines suffer from high memory fragmentation, limited parallelism, and inefficient GPU utilization. vLLM, an open-source project from UC Berkeley’s SkyLab (and now widely adopted in industry), has rapidly become the go-to solution for high-throughput, high-efficiency LLM inference.

Why LLM inference at scale is challenging

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LLMs are computationally expensive. Serving even a single model instance can demand tens of gigabytes of GPU memory. Scaling to millions of requests per day introduces bottlenecks in:​

• KV cache management: Transformers store key/value (KV) tensors for every token. This rapidly consumes memory and leads to fragmentation.​

• Batching inefficiencies: Dynamic batching across unrelated requests is complex. Conventional engines pad sequences or wait for batch fill-up, wasting compute.​

• Latency–throughput trade-offs: Improving throughput often increases latency. Enterprises need both — sub-second responses and the ability to serve thousands of queries per GPU.​

• Memory bandwidth constraints: As models grow (LLaMA-3, Mistral, Qwen2), the KV cache becomes the primary bottleneck, not compute.​

• Multi-tenancy​: Serving multiple models simultaneously or handling multiple clients with different SLAs increases server pressure.​

vLLM and the innovation of paged attention​

At its core, vLLM introduces a virtual memory abstraction for attention, similar to how operating systems manage RAM.​

1. Paged attention: divides the KV cache into smaller fixed sizeblocks (“pages”). Instead of allocating a contiguous chunk of memory per sequence, vLLM stores KV data in these pages and dynamically reassigns them as sequences grow. ​

2. Continuous batching: vLLM also supports continuous(dynamic) batching, where incoming requests are added mid-execution without restarting batches. This leads to:​

   1. 2x to 4x higher throughput​

   2. Minimal scheduling overhead​

   3. Superior performance for chat-style workloads with streaming tokens​

3. Speculative decoding and multi-model serving: Recent vLLM versions add:​

   1. Speculative decoding (using a small draft model to accelerate large model predictions)​

   2. Parallel model execution (multiple models sharing GPU memory)​

   3. LoRA, QLoRA, and MoE support​

   4. Tensor parallelism for multi-GPU scaling​

Deploying vLLM in production

•       vLLM Server: Hosts models & performs inference

•       Load Balancer: Distributes requests based on SLA & traffic.

•       Model Registry: Stores & versions model artifacts

•       AutoScaler: Adjusts GPU replicas based on tokens/se

•       Observability Stack: Logs, metrics & health probes

Deployment options

•       On Prem: Ideal for A100/H100 clusters

•       Kubernetes (EKS/AKS): GPU pool nodes, autoscaling, Prometheus monitoring.

•       Cloud: AWS Sagemaker, GCP Vertex AI (vLLM supported natively)

•       Serverless GPU