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

Tencent Hunyuan Production Fundamentals

• Overview of key scenarios for serving Tencent Hunyuan models.
• Characteristics of large and MoE models in production settings.
• Identification of common bottlenecks in latency, throughput, and cost.
• Defining service-level objectives (SLOs) for inference workloads.

Deployment Architecture and Serving Flow

• Core components of a production-grade inference stack.
• Evaluating containerized, on-premise, and cloud deployment models.
• Basics of model loading, request routing, and GPU allocation.
• Designing systems for reliability and operational simplicity.

Latency Optimization in Practice

• Leveraging optimized inference engines such as TensorRT where appropriate.
• Understanding KV-cache concepts and applying practical cache tuning.
• Minimizing startup, warmup, and response overhead.
• Measuring time to first token and token generation speed.

Throughput, Batching, and GPU Efficiency

• Strategies for continuous and request batching.
• Managing concurrency and queue behavior effectively.
• Enhancing GPU utilization while preserving user experience.
• Handling requests with long contexts and mixed workloads.

Quantization and Cost Control

• The importance of quantization in production serving.
• Practical trade-offs between FP16, INT8, and other precision options.
• Balancing model quality, latency, and infrastructure costs.
• Developing a simple checklist for cost optimization.

Operations, Monitoring, and Readiness Review

• Configuring autoscaling triggers for inference services.
• Monitoring key metrics including latency, throughput, cache usage, and GPU health.
• Implementing logging, alerting, and incident response protocols.
• Reviewing a reference deployment and formulating an improvement plan.