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

Tencent Hunyuan Production Fundamentals

• Overview of serving scenarios for Tencent Hunyuan models.
• Production characteristics specific to large and MoE models.
• Identifying common bottlenecks related to latency, throughput, and cost.
• Establishing 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.

Practical Latency Optimization

• Utilizing optimized inference engines like TensorRT where applicable.
• 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 batching and request batching.
• Managing concurrency and queue behavior.
• Enhancing GPU utilization while maintaining user experience.
• Processing long-context and mixed-workload requests.

Quantization and Cost Management

• 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 basic 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.
• Essentials of logging, alerting, and incident response.
• Reviewing reference deployments and formulating improvement plans.