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

Tencent Hunyuan Production Fundamentals

• Overview of model serving scenarios for Tencent Hunyuan.
• Production characteristics of large and MoE models.
• Common bottlenecks related to latency, throughput, and cost.
• Defining service-level objectives for inference workloads.

Deployment Architecture and Serving Flow

• Core components of a production inference stack.
• Evaluating 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

• Leveraging optimized inference engines like TensorRT where applicable.
• 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.
• Improving 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 between 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.
• Fundamentals of logging, alerting, and incident response.
• Reviewing a reference deployment and creating an improvement plan.