Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization Training Course
Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization is a practical course focused on the reliable deployment of Tencent Hunyuan models at scale.
This instructor-led, live training (online or onsite) is intended for intermediate-level engineers and architects seeking to deploy large and MoE models with reduced latency, enhanced GPU utilization, and controlled operating costs.
Upon completion, participants will be able to:
- Identify the primary production challenges associated with serving Tencent Hunyuan models.
- Apply inference optimization techniques, including TensorRT, KV-cache tuning, quantization, and batching.
- Design scalable deployment strategies incorporating autoscaling, monitoring, and capacity planning.
- Optimize latency and cost trade-offs for real-world production workloads.
Course Format
- Interactive lectures and discussion.
- Extensive exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request customized training for government, please contact us to arrange.
Course Outline
Tencent Hunyuan Production Fundamentals
- Overview of Tencent Hunyuan model deployment scenarios
- Production characteristics of large-scale and Mixture-of-Experts (MoE) models
- Common bottlenecks in latency, throughput, and cost for government workloads
- Defining service-level objectives for inference tasks
Deployment Architecture and Serving Flow
- Core components of a production inference stack
- Selecting containerized, on-premise, or cloud deployment models
- Model loading, request routing, and GPU allocation principles
- Designing for reliability and operational simplicity for government
Latency Optimization in Practice
- Implementing optimized inference engines such as TensorRT where applicable
- KV-cache concepts and practical cache tuning strategies
- Mitigating startup, warmup, and response overhead
- Measuring time to first token and token generation speed
Throughput, Batching, and GPU Efficiency
- Continuous batching and request batching methodologies
- Managing concurrency and queue behavior
- Enhancing GPU utilization while maintaining user experience standards
- Handling long-context and mixed-workload requests
Quantization and Cost Control
- The importance of quantization for production serving
- Practical trade-offs of FP16, INT8, and other precision options
- Balancing model quality, latency, and infrastructure cost
- Developing a cost optimization checklist for government
Operations, Monitoring, and Readiness Review
- Autoscaling triggers for inference services
- Monitoring latency, throughput, cache usage, and GPU health
- Logging, alerting, and incident response protocols
- Reviewing reference deployments and establishing improvement plans
Requirements
- Foundational knowledge of large language model deployment and inference workflows
- Experience with containers, cloud or on-premise infrastructure, and API-based services
- Proficiency in Python or system engineering tasks
Audience
- ML engineers deploying LLMs into production
- Platform engineers responsible for GPU-based inference services
- Solution architects designing scalable AI serving platforms
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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