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.
• Selecting between containerized, on-premise, and cloud deployment models.
• Foundations of model loading, request routing, and GPU allocation.
• Designing for reliability and operational simplicity.

Practical Latency Optimization

• Utilizing optimized inference engines like TensorRT where applicable.
• KV-cache concepts and practical cache tuning techniques.
• 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 effectively.
• Enhancing GPU utilization without compromising user experience.
• Handling long-context and mixed-workload requests.

Quantization and Cost Control

• Understanding the importance of quantization for production serving.
• Evaluating practical trade-offs of FP16, INT8, and other precision options.
• Balancing model quality, latency, and infrastructure costs.
• Developing a simple cost optimization checklist.

Operations, Monitoring, and Readiness Review

• Autoscaling triggers for inference services.
• Monitoring metrics including latency, throughput, cache usage, and GPU health.
• Basics of logging, alerting, and incident response.
• Reviewing a reference deployment and creating an improvement plan.