Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization Training Course
This practical course focuses on the reliable deployment and serving of Tencent Hunyuan models at scale.
Delivered as a live, instructor-led session (either online or onsite), it is designed for intermediate-level engineers and architects seeking to leverage Tencent Hunyuan for deploying large-scale and MoE models with reduced latency, enhanced GPU utilization, and tightly controlled operational costs.
Upon completion, participants will be equipped to:
- Identify and articulate the primary production challenges associated with serving Tencent Hunyuan models.
- Implement practical inference optimizations, including the use of TensorRT, KV-cache tuning, quantization, and batching strategies.
- Architect a scalable deployment strategy that incorporates autoscaling, comprehensive monitoring, and robust capacity planning.
- Optimize the balance between latency and cost for real-world production workloads.
Course Format
- Interactive lectures paired with facilitated discussions.
- Extensive exercises and practical application tasks.
- Hands-on implementation exercises in a live laboratory environment.
Customization Options
- Please contact us to discuss requirements for a tailored training experience.
Course Outline
Tencent Hunyuan Production Fundamentals
- Overview of typical serving scenarios for Tencent Hunyuan models
- Key production characteristics of large and MoE architectures
- Common bottlenecks affecting latency, throughput, and cost
- Establishing service-level objectives for inference workloads
Deployment Architecture and Serving Workflow
- Essential components of a production-grade inference stack
- Comparing containerized, on-premise, and cloud deployment models
- Fundamentals of model loading, request routing, and GPU allocation
- Designing for reliability and operational simplicity
Practical Latency Optimization
- Leveraging optimized inference engines such as TensorRT where applicable
- KV-cache concepts and strategies for practical cache tuning
- Mitigating startup, warmup, and response overhead
- Measuring time to first token and token generation speed
Throughput, Batching, and GPU Efficiency
- Implementing continuous batching and request batching strategies
- Managing concurrency and queue behavior effectively
- Enhancing GPU utilization without compromising user experience
- Handling long-context and mixed-workload requests
Quantization and Cost Management
- The importance of quantization for production serving
- Evaluating trade-offs of FP16, INT8, and other precision options
- Balancing model quality, latency, and infrastructure expenditure
- Developing a practical cost optimization checklist
Operations, Monitoring, and Readiness Assessment
- Configuring autoscaling triggers for inference services
- Monitoring latency, throughput, cache usage, and GPU health
- Essentials of logging, alerting, and incident response
- Reviewing a reference deployment and formulating an improvement plan
Requirements
- Fundamental understanding of large language model deployment and inference workflows.
- Hands-on experience with containers, cloud or on-premise infrastructure, and API-driven services.
- Proficiency in Python or system engineering tasks.
Intended Audience
- ML engineers responsible for integrating LLMs into production environments.
- Platform engineers managing GPU-based inference services.
- Solution architects designing scalable AI serving platforms.
Open Training Courses require 5+ participants.
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