Deploying Tencent Hunyuan in Production: Low-Latency Inference & Cost Optimization Training Course
Deploying Tencent Hunyuan in Production: Optimising for Low Latency and Cost Efficiency is a hands-on course designed to help organisations serve Tencent Hunyuan models reliably at scale.
This instructor-led, live training (available online or onsite) is tailored for intermediate-level engineers and architects looking to deploy large language and Mixture of Experts (MoE) models with reduced latency, enhanced GPU utilisation, and managed operational costs.
Upon completion of this training, participants will be able to:
- identify the key challenges associated with serving Tencent Hunyuan models in a production environment.
- implement practical inference optimisation techniques, including TensorRT, KV-cache tuning, quantisation, and batching.
- design a scalable deployment strategy incorporating autoscaling, monitoring, and capacity planning.
- balance latency and cost trade-offs effectively for real-world production workloads.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live lab environment.
Course Customisation Options
- To request a customised training session for this course, please contact us to arrange your schedule.
Course Outline
Tencent Hunyuan Production Fundamentals
- Overview of Tencent Hunyuan model serving scenarios.
- Production characteristics of large and MoE models.
- Common latency, throughput, and cost bottlenecks.
- Defining service-level objectives for inference workloads.
Deployment Architecture and Serving Flow
- Core components of a production inference stack.
- Choosing between containerised, on-premise, and cloud deployment models.
- Model loading, request routing, and GPU allocation basics.
- Designing for reliability and operational simplicity.
Latency Optimization in Practice
- Using optimised inference engines such as TensorRT where applicable.
- 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
- Continuous batching and request batching strategies.
- Managing concurrency and queue behavior.
- Improving GPU utilization without harming user experience.
- Handling long-context and mixed-workload requests.
Quantization and Cost Control
- Why quantization matters for production serving.
- Practical trade-offs of FP16, INT8, and other common precision options.
- Balancing model quality, latency, and infrastructure cost.
- Building a simple cost optimization checklist.
Operations, Monitoring, and Readiness Review
- Autoscaling triggers for inference services.
- Monitoring latency, throughput, cache usage, and GPU health.
- Logging, alerting, and incident response basics.
- Reviewing a reference deployment and creating an improvement plan.
Requirements
- Fundamental understanding 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 environments.
- Platform engineers responsible for managing GPU-based inference services.
- Solution architects designing scalable AI serving platforms.
Open Training Courses require 5+ participants.
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