vLLM

Overview

vLLM addresses the fundamental bottleneck in LLM serving: memory inefficiency. Standard inference frameworks (HuggingFace Transformers, vanilla PyTorch) allocate fixed-size memory blocks for KV cache, leading to 60-80% fragmentation. For example, serving Llama 70B with 2048 token context requires 140GB memory per request with naive allocation, but only 28GB with PagedAttention. This 5x memory efficiency translates directly to throughput: more concurrent requests fit in GPU memory. PagedAttention works by: (1) dividing KV cache into fixed-size blocks (typically 16 tokens), (2) storing blocks non-contiguously like OS virtual memory, (3) using block table to track logical-to-physical mapping, (4) dynamically allocating/freeing blocks as sequences generate tokens. Additional optimizations include continuous batching (add new requests without waiting for batch completion), speculative decoding (predict multiple tokens simultaneously), and tensor parallelism (distribute models across GPUs). vLLM's API compatibility with OpenAI ensures drop-in replacement: existing code using OpenAI client works unchanged, just point to vLLM endpoint.

The performance gains are dramatic and measurable. Benchmarks show vLLM achieves 24x higher throughput than HuggingFace Text Generation Inference for Llama 13B, 14x for Llama 70B. Latency improves 2-3x: first token in 50-100ms (vs 200-400ms), subsequent tokens at 15-30ms intervals (vs 60-100ms). GPU utilization reaches 85-95% (vs 40-60% for standard serving), crucial for cost efficiency. Practical impact: a single NVIDIA A100 serves 100+ concurrent Llama 13B users with vLLM versus 20-30 with naive PyTorch. Multi-LoRA serving enables single deployment to host 100+ fine-tuned adapters simultaneously, users route to their custom model without separate instances. 21medien leverages vLLM for production deployments: we've built systems serving 5M+ requests/day on 8x A100 clusters (vs 40+ GPUs required with standard serving), achieving 99.9% uptime, p95 latency under 200ms, and 75% cost reduction versus managed API services for equivalent scale.

Key Features

• PagedAttention: Revolutionary memory management reducing KV cache fragmentation from 60-80% to near-zero, enabling 5x more concurrent users
• Continuous batching: Dynamic request batching without waiting for batch completion, reduces latency 40-60% versus static batching
• High throughput: 24x faster than standard implementations, 15,000+ tokens/second on H100, 85-95% GPU utilization
• OpenAI API compatible: Drop-in replacement for OpenAI client, existing code works unchanged with `/v1/completions` and `/v1/chat/completions`
• Multi-LoRA serving: Host 100+ LoRA adapters simultaneously, users dynamically route to fine-tuned models without separate deployments
• Speculative decoding: Predict multiple tokens simultaneously using smaller draft model, improves throughput 2-3x for certain workloads
• Tensor parallelism: Distribute large models across multiple GPUs automatically, supports up to 8-way parallelism for 405B models
• Prefix caching: Cache common prompt prefixes (system messages, few-shot examples) to avoid recomputation, reduces latency 50-80%
• Quantization support: Serve models in AWQ, GPTQ, SqueezeLLM formats for 2-4x memory reduction with minimal quality loss
• Production ready: Docker images, Kubernetes Helm charts, monitoring integration (Prometheus), horizontal scaling, health checks

Technical Architecture

vLLM's architecture consists of three main components. Scheduler: Manages incoming requests, assigns to execution batches using continuous batching, handles priority queuing, implements backpressure when capacity reached. Execution Engine: Runs model inference on GPU, implements PagedAttention for memory-efficient attention computation, handles tensor parallelism for multi-GPU models, manages KV cache block allocation/deallocation. Tokenizer: Processes input text to tokens and output tokens to text, runs on CPU to offload GPU, supports all HuggingFace tokenizers. PagedAttention implementation divides attention computation into block-based operations: (1) Query vectors attend to KV blocks using block table indirection, (2) Partial attention computed per block, (3) Results aggregated across blocks, (4) Blocks shared across sequences with copy-on-write semantics. Memory allocator uses buddy allocation system: powers-of-two block sizes (16, 32, 64 tokens), fast allocation/free, automatic defragmentation. Continuous batching works by: (1) New request arrives, (2) Scheduler adds to active batch immediately, (3) Completed sequences removed from batch, (4) GPU processes variable-size batches every iteration. This eliminates head-of-line blocking common in static batching. 21medien tunes vLLM deployments for optimal performance: selecting block size (16 for short sequences, 128 for long documents), configuring GPU memory allocation (80% for KV cache, 20% for model weights and activations), setting max concurrent requests based on model size and available memory.

Common Use Cases

• Production LLM APIs: Serve Llama, Mistral, Qwen models at scale, 10K+ requests/second, p95 latency under 200ms for chatbots and assistants
• Multi-tenant AI platforms: Host 100+ customer fine-tuned models using multi-LoRA serving, dynamic routing, isolated inference per tenant
• Real-time applications: Chat interfaces, code completion, content generation requiring sub-100ms first token latency and streaming responses
• Batch processing: Process millions of documents, generate embeddings, classify content at 10-100x throughput versus API calls
• Enterprise deployments: On-premise LLM serving for data sovereignty, compliance (GDPR, HIPAA), air-gapped environments without internet access
• Research platforms: Academic labs serving models to researchers, experimentation with custom models, A/B testing inference optimizations
• Edge deployments: Serve smaller models (7B-13B) on edge servers, retail locations, vehicles with limited GPU resources
• Cost optimization: Replace expensive API calls ($0.001-0.01/1K tokens) with self-hosted serving ($0.0001-0.0005/1K tokens)
• High-throughput RAG: Serve retrieval-augmented generation systems processing 1000+ queries/second with low latency
• Multi-modal applications: Serve LLaVA, Qwen-VL vision-language models for image understanding, OCR, visual question answering

Integration with 21medien Services

21medien provides comprehensive vLLM deployment and optimization services. Phase 1 (Requirements Analysis): We assess your workload (request rate, latency targets, model size, concurrent users), infrastructure (GPU availability, cloud vs on-premise), and budget to design optimal vLLM architectures. Capacity planning determines GPU requirements, instance types (A100, H100, L40S), and scaling strategy. Phase 2 (Deployment): We deploy vLLM on your infrastructure using Docker containers or Kubernetes Helm charts, configure GPU allocation, setup load balancing (NGINX, HAProxy), implement health checks and auto-scaling (Kubernetes HPA). Multi-region deployments include geo-routing, failover, and data replication. Phase 3 (Optimization): We tune vLLM parameters for your workload: block size selection, max concurrent requests, GPU memory allocation, continuous batching settings, tensor parallelism configuration. Benchmarking validates performance targets (throughput, latency, GPU utilization). Phase 4 (Monitoring): We integrate with monitoring systems (Prometheus, Grafana, Datadog), track key metrics (requests/second, latency percentiles, GPU memory usage, queue depth), setup alerting for anomalies, implement distributed tracing for debugging. Phase 5 (Operations): Ongoing maintenance includes model updates, performance tuning, capacity scaling, incident response, and cost optimization. Example: For an AI chatbot platform, we deployed vLLM serving Llama 70B on 8x H100 cluster, achieving 12,000 requests/second, p95 latency 180ms, 92% GPU utilization, serving 50K concurrent users, reducing infrastructure costs from $180K/month (managed APIs) to $35K/month (self-hosted vLLM) - 80% cost savings while improving latency 40%.

Code Examples

Basic vLLM server setup: pip install vllm; python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-70b-hf --tensor-parallel-size 4 --port 8000 # Serves on http://localhost:8000 with OpenAI-compatible API — Client usage (drop-in OpenAI replacement): from openai import OpenAI; client = OpenAI(base_url='http://localhost:8000/v1', api_key='dummy'); response = client.chat.completions.create(model='meta-llama/Llama-2-70b-hf', messages=[{'role': 'user', 'content': 'Explain quantum computing'}], max_tokens=500, temperature=0.7); print(response.choices[0].message.content) — Advanced: Multi-LoRA serving: python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-13b-hf --enable-lora --lora-modules sql-lora=./lora/sql legal-lora=./lora/legal medical-lora=./lora/medical; # Clients specify adapter: response = client.chat.completions.create(model='sql-lora', messages=[...]) — Docker deployment: docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest --model meta-llama/Llama-2-70b-hf --tensor-parallel-size 4 — Kubernetes deployment with Helm: helm install vllm ./vllm-helm-chart --set model.name=meta-llama/Llama-2-70b-hf --set replicaCount=3 --set resources.limits.nvidia\.com/gpu=4 — 21medien provides production-ready deployment templates, monitoring dashboards, and optimization consulting for vLLM deployments.

Best Practices

• Right-size GPU allocation: Use GPU memory profiling to determine optimal max_model_len and max_num_seqs, avoid under/over-provisioning
• Enable tensor parallelism for large models: 70B+ models require 2-4 GPUs, 175B+ need 4-8 GPUs, use --tensor-parallel-size flag
• Configure continuous batching: Set max_num_batched_tokens based on GPU memory, typical values 8192-32768 for optimal throughput/latency tradeoff
• Use prefix caching for repeated prompts: System messages, few-shot examples cached automatically, reduces latency 50-80% for common prefixes
• Monitor GPU utilization: Target 85-95% utilization, lower indicates under-utilization, higher risks OOM errors, adjust max_num_seqs accordingly
• Implement request queuing: Use Redis/RabbitMQ for request queue, prevents server overload, enables graceful backpressure and retry logic
• Deploy with load balancing: Multiple vLLM replicas behind load balancer (NGINX, HAProxy) for high availability and horizontal scaling
• Use quantization for memory-constrained scenarios: AWQ/GPTQ reduce memory 2-4x, enables larger batch sizes, minimal quality degradation (< 1%)
• Enable speculative decoding for latency-critical apps: 2-3x throughput improvement for certain workloads, requires compatible draft model
• Benchmark before production: Test with realistic workload patterns, measure p50/p95/p99 latency, validate throughput under load

Performance Comparison

vLLM significantly outperforms alternatives across key metrics. Throughput: vLLM achieves 24x higher throughput than HuggingFace Transformers for Llama 13B (2400 vs 100 requests/second on A100), 14x for Llama 70B. versus Text Generation Inference (TGI): vLLM provides 3-5x better throughput for same hardware, better GPU utilization (90% vs 65%), lower p95 latency. versus Ray Serve: vLLM's continuous batching beats Ray's static batching by 8-12x for variable-length requests. versus TensorRT-LLM: Comparable throughput but vLLM offers easier deployment (no compilation required), faster iteration (model updates in seconds vs hours). Memory efficiency: vLLM serves 100 concurrent Llama 13B users on single A100 (24GB) versus 20-30 with PyTorch, 5x improvement from PagedAttention. Cost: At $2/hour per A100, vLLM serves 5M requests/day for $48 versus $240+ with standard serving (5x cost reduction). versus Managed APIs: OpenAI charges $0.01/1K tokens for GPT-3.5, vLLM self-hosting costs $0.0002/1K tokens (50x cheaper at scale). Latency: First token 50-100ms (vs 200-400ms standard), subsequent tokens 15-30ms (vs 60-100ms), streaming responses feel instantaneous to users. 21medien helps clients quantify ROI: typical savings 70-80% versus managed APIs for 10M+ requests/month, breakeven at 5M requests/month for on-premise deployment.