DeepSeek-VLLM Training in San Francisco

DeepSeek-VLLM Training in San Francisco, USA

Participants will learn how to reduce latency and optimize memory usage

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DeepSeek-VLLM Training is a professional training program delivered by ProgNXT in San Francisco, USA. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this DeepSeek-VLLM Training course with hands-on, instructor-led sessions. This training on DeepSeek-VLLM focuses on efficient inference, optimization, and large-scale deployment of LLMs. Participants will learn how to...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: XBY41
21 Hrs
Last Updated: April 2026

Course Rating5/5

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.

Welcome to the official DeepSeek-VLLM Training certification program in San Francisco, USA. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Experience with Python and deep learning frameworks (PyTorch, TensorFlow)
Familiarity with LLM deployment concepts
Basic understanding of Kubernetes and cloud infrastructure (optional but helpful)

Participants will develop skills in optimizing LLM inference using DeepSeek-VLLM, improving performance, and scaling models efficiently.

DeepSeek-VLLM Architecture: Understanding memory-efficient LLM inference.
Optimized LLM Deployment: Running large models with minimal hardware.
Latency Reduction Techniques: Implementing kernel fusion, tensor parallelism, and model quantization.
Scaling LLMs: Deploying and managing models on cloud infrastructure.
Integration with ML Pipelines: Using DeepSeek-VLLM in production workflows.

Participants will be assessed on:

DeepSeek-VLLM Setup: Installing and configuring DeepSeek-VLLM.
Performance Optimization: Implementing optimizations for latency and efficiency.
Scaling Strategies: Deploying LLMs at scale in cloud environments.
Resource Management: Reducing GPU/CPU overhead during inference.
Use Case Implementations: Running real-world applications with DeepSeek-VLLM.

Participants who complete the training and actively engage in exercises will receive a ProgNXT Certificate of Completion.

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

Course slides and documentation
DeepSeek-VLLM optimization scripts and deployment guides
Sample examples for large-scale LLM inference

Participants will complete a final project on optimizing LLM inference using DeepSeek-VLLM.
Successful candidates will receive the ProgNXT Certificate of Completion.

DeepSeek-VLLM Training Events in san-francisco

Online Events

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More Upcoming Session Dates

October 2026

📍 San Francisco, USA

19th Oct 2026→21st Oct 2026

🌐 Online Instructor-Led

26th Oct 2026→28th Oct 2026

November 2026

📍 San Francisco, USA

5th Nov 2026→7th Nov 2026

🌐 Online Instructor-Led

8th Nov 2026→10th Nov 2026

📍 San Francisco, USA

24th Nov 2026→26th Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→29th Nov 2026

December 2026

📍 San Francisco, USA

2nd Dec 2026→4th Dec 2026

🌐 Online Instructor-Led

5th Dec 2026→7th Dec 2026

📍 San Francisco, USA

25th Dec 2026→27th Dec 2026

🌐 Online Instructor-Led

28th Dec 2026→30th Dec 2026

Events for other courses in San Francisco, USA

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DeepSeek-VLLM Training in San Francisco