DeepSeek-VLLM Training in New York, USA

This training on DeepSeek-VLLM focuses on efficient inference, optimization, and large-scale deployment of LLMs. Participants will learn how to reduce latency, optimize memory usage, and scale LLM inference for production applications using DeepSeek-VLLM.

Introduction to DeepSeek-VLLM & Optimized Inference

• Module 1: Understanding DeepSeek-VLLM
  • Overview of LLM inference challenges
  • DeepSeek-VLLM architecture and efficiency techniques
  • Comparing DeepSeek-VLLM with traditional inference methods
• Module 2: Setting Up DeepSeek-VLLM
  • Installation and configuration on local/cloud servers
  • Running inference with optimized LLMs
  • Profiling model performance

Optimizing Inference & Reducing Latency

• Module 3: Optimization Techniques for LLM Inference
  • Model quantization and kernel fusion techniques
  • Tensor parallelism and activation checkpointing
  • Memory-efficient batch processing
• Module 4: Scaling DeepSeek-VLLM for Large-Scale Applications
  • Deploying on Kubernetes and serverless environments
  • Handling multi-GPU and multi-node setups
  • Monitoring and debugging inference performance

Integration & Real-World Deployment

• Module 5: Deploying DeepSeek-VLLM in Production
  • Integrating optimized inference into ML pipelines
  • Building scalable APIs for real-time LLM applications
  • Best practices for cloud and on-premise deployments
• Module 6: Case Studies & Future Trends
  • Implementing DeepSeek-VLLM for chatbots, summarization, and search applications
  • Future of efficient LLM inference and model distillation
  • Emerging research in inference optimization
• Hands-On Labs