Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization Training Course

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