vLLM Technical Expertise

I specialize in scalable, performance-tuned vLLM API systems:

  • vLLM Core: PagedAttention, GPU memory optimization, server performance tuning
  • Inference Optimization: Throughput tuning, latency reduction, load handling
  • Caching Systems: KV cache control, request deduplication, memory-efficient storage
  • Dynamic Batching: Queued request processing, adaptive batch sizes, response grouping
  • API Design: Secure endpoints, auth layers, WebSocket support, async endpoints
  • Production Infrastructure: Docker, Kubernetes, horizontal scaling, health checks
  • Monitoring & Observability: Prometheus/Grafana metrics, request tracing, logs, and error alerts

vLLM Implementation Examples

  • High-Throughput APIs: Concurrent inference with GPU batching and low-latency caching
  • Dynamic Batching Engine: Real-time batching for chat and API endpoints with smart queuing
  • Memory-Aware Serving: Use of PagedAttention for large model serving within memory limits
  • Cloud-Native Deployment: Scalable vLLM clusters deployed via Kubernetes with autoscaling
  • Multi-Model Orchestration: Route requests across multiple vLLM models with version control
  • API Access Layer: REST/GraphQL APIs with integrated auth and rate limiting

Development Process

  1. Model & Infra Analysis Evaluate models, memory needs, and infrastructure for vLLM deployment.

  2. Server & API Design Build vLLM server stack with API endpoints, caching, batching, and routing logic.

  3. Production Deployment Deploy into containerized environments with full monitoring and scaling setup.

Investment & Pricing

Pricing based on infrastructure complexity and performance targets:

  • Basic vLLM Deployment: $20K–40K Single model deployment with optimized API server

  • Advanced vLLM Platform: $40K–80K Multi-model vLLM server with batching, caching, and monitoring

  • Enterprise vLLM System: $80K–150K+ Large-scale LLM orchestration with Kubernetes, HA, and analytics

  • R&D or Support: $150–250/hour Performance tuning, advanced batching, or integration

  • Ongoing Optimization: Monthly tuning, log analysis, and model upgrades

See vLLM in Action

Try a live demo to experience how optimized vLLM serving accelerates your AI system. From blazing fast inference to scalable multi-model serving.

Ready to Build Your vLLM Platform?

Let’s discuss your vLLM infrastructure goals, performance bottlenecks, or model deployment needs. I help enterprises build the fastest, most efficient inference stacks for LLMs in production.