Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization Training Course
Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization is a practical course on serving Tencent Hunyuan models reliably at scale.
This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and architects who wish to use Tencent Hunyuan to deploy large and MoE models with lower latency, better GPU utilization, and controlled operating cost.
By the end of this training, participants will be able to:
- explain the main production challenges of serving Tencent Hunyuan models.
- apply practical inference optimization techniques such as TensorRT, KV-cache tuning, quantization, and batching.
- design a scalable deployment approach with autoscaling, monitoring, and capacity planning.
- improve latency and cost trade-offs for real production workloads.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Tencent Hunyuan Production Fundamentals
- Overview of Tencent Hunyuan model serving scenarios
- Production characteristics of large and MoE models
- Common latency, throughput, and cost bottlenecks
- Defining service-level objectives for inference workloads
Deployment Architecture and Serving Flow
- Core components of a production inference stack
- Choosing between containerized, on-premise, and cloud deployment models
- Model loading, request routing, and GPU allocation basics
- Designing for reliability and operational simplicity
Latency Optimization in Practice
- Using optimized inference engines such as TensorRT where applicable
- 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
- Continuous batching and request batching strategies
- Managing concurrency and queue behavior
- Improving GPU utilization without harming user experience
- Handling long-context and mixed-workload requests
Quantization and Cost Control
- Why quantization matters for production serving
- Practical trade-offs of FP16, INT8, and other common precision options
- Balancing model quality, latency, and infrastructure cost
- Building a simple cost optimization checklist
Operations, Monitoring, and Readiness Review
- Autoscaling triggers for inference services
- Monitoring latency, throughput, cache usage, and GPU health
- Logging, alerting, and incident response basics
- Reviewing a reference deployment and creating an improvement plan
Requirements
- Basic understanding of large language model deployment and inference workflows
- Experience with containers, cloud or on-premise infrastructure, and API-based services
- Working knowledge of Python or system engineering tasks
Audience
- ML engineers deploying LLMs into production
- Platform engineers responsible for GPU-based inference services
- Solution architects designing scalable AI serving platforms
Open Training Courses require 5+ participants.
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