Core Service
Model Deployment
vLLM, TensorRT-LLM, TGI, KServe. Autoscaling, canary, observability
Overview
Model deployment transforms experimental AI into production-ready systems that scale reliably under real-world load. Our deployment expertise ensures your models serve predictions with low latency, high throughput, and predictable costs—leveraging cutting-edge serving frameworks, GPU optimization, and MLOps best practices for enterprise-grade reliability.
Key Features
GPU Packing & Quantization
Maximize GPU utilization with multi-model serving, AWQ/GPTQ quantization, and PagedAttention for 2-4x throughput improvements without sacrificing accuracy.
KV Caching & Streaming
Efficient key-value cache management and token streaming for sub-50ms first-token latency and smooth user experiences in interactive applications.
Autoscaling & Cost Controls
Horizontal and vertical autoscaling based on queue depth, latency SLOs, and cost budgets—scaling from zero to hundreds of replicas on demand.
Canary Deployments
Progressive rollouts with automated rollback on regression—test new model versions on small traffic percentages before full deployment.
Technical Approach
Our deployment methodology prioritizes reliability and performance:
- Serving Framework Selection: Choose optimal framework (vLLM, TGI, TensorRT-LLM) based on model architecture and latency requirements
- Optimization: Apply quantization, fusion, and compilation for maximum throughput on target hardware
- Infrastructure Setup: Configure Kubernetes, KServe, or Ray Serve with GPU node pools and networking
- Observability: Implement metrics, logging, and tracing for latency, throughput, error rates, and resource utilization
- Load Testing: Stress test with realistic traffic patterns to validate SLOs before production launch
Use Cases
Production model deployment across diverse workloads:
- LLM APIs: Serve language models for chatbots, content generation, and semantic search with streaming responses
- Computer Vision: Real-time object detection, classification, and segmentation for video analytics and robotics
- Recommendation Systems: Low-latency personalized recommendations at scale for e-commerce and media platforms
- Embedding Services: High-throughput text/image embedding generation for RAG and similarity search
Expected Outcomes
Model deployment delivers production-ready AI infrastructure:
- 99.9% uptime with automated failover and health checks
- P95 latency <100ms for real-time applications
- 30-50% cost reduction through GPU optimization and autoscaling
- Zero-downtime deployments with canary rollouts and rollback
Ready to Deploy Production AI?
Let's discuss how our deployment expertise can get your models into production with confidence.