Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization Training Course
Implementing Tencent Hunyuan in Production: Minimizing Latency and Optimizing Costs is a hands-on course focused on the reliable, large-scale deployment of Tencent Hunyuan models.
This instructor-led, live training (available online or onsite) targets intermediate-level engineers and architects looking to deploy large and MoE (Mixture of Experts) models using Tencent Hunyuan. The goal is to achieve lower latency, enhanced GPU utilization, and controlled operational expenses.
Upon completion of this training, participants will be equipped to:
- Articulate the primary production challenges associated with serving Tencent Hunyuan models.
- Apply practical inference optimization techniques, including TensorRT, KV-cache tuning, quantization, and batching.
- Design a scalable deployment strategy featuring autoscaling, monitoring, and capacity planning.
- Optimize the balance between latency and cost for real-world production workloads.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request a customized training session for this course, please get in touch with us to arrange details.
Course Outline
Tencent Hunyuan Production Fundamentals
- Overview of Tencent Hunyuan model serving scenarios.
- Production characteristics of large and MoE models.
- Common bottlenecks regarding latency, throughput, and cost.
- Defining service-level objectives for inference workloads.
Deployment Architecture and Serving Flow
- Core components of a production inference stack.
- Selecting between containerized, on-premise, and cloud deployment models.
- Fundamentals of model loading, request routing, and GPU allocation.
- Designing for reliability and operational simplicity.
Latency Optimization in Practice
- Utilizing optimized inference engines such as TensorRT where appropriate.
- Understanding KV-cache concepts and practical cache tuning.
- Reducing startup, warmup, and response overhead.
- Measuring time to first token and token generation speed.
Throughput, Batching, and GPU Efficiency
- Strategies for continuous batching and request batching.
- Managing concurrency and queue behavior.
- Enhancing GPU utilization without compromising user experience.
- Handling long-context and mixed-workload requests.
Quantization and Cost Control
- The importance of quantization for production serving.
- Practical trade-offs of FP16, INT8, and other common precision options.
- Balancing model quality, latency, and infrastructure cost.
- Creating a simple checklist for cost optimization.
Operations, Monitoring, and Readiness Review
- Autoscaling triggers for inference services.
- Monitoring latency, throughput, cache usage, and GPU health.
- Basics of logging, alerting, and incident response.
- Reviewing a reference deployment and developing an improvement plan.
Requirements
- Fundamental understanding of large language model deployment and inference workflows.
- Experience with containers, cloud or on-premise infrastructure, and API-based services.
- Proficiency in Python or experience with system engineering tasks.
Audience
- Machine learning engineers deploying LLMs into production.
- Platform engineers responsible for GPU-based inference services.
- Solution architects designing scalable AI serving platforms.
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