Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training TensorRT-LLM 2026 - Optimizing LLM Inferences in Production training are: Python basics, PyTorch or TensorFlow, basic knowledge of LLMs and CUDA. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training TensorRT-LLM 2026 - Optimizing LLM Inferences in Production training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training TensorRT-LLM 2026 - Optimizing LLM Infe... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training TensorRT-LLM 2026 - Optimizing LLM Inferences in Production training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training TensorRT-LLM 2026 - Optimizing LLM Inferences in Production training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training TensorRT-LLM 2026 - Optimizing LLM Inferences in Production training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master the installation and configuration of TensorRT-LLM for professional projects

Develop certified skills in optimizing LLM inferences

Implement enterprise-tailored TensorRT-LLM pipelines

Design accelerated models with TensorRT-LLM at the beginner level

Optimize GPU performance for fast and scalable inferences

Deploy TensorRT-LLM applications in secure production environments

Training program

Module 1TensorRT-LLM Fundamentals: Installation and First Models (NVIDIA Docker, LoRA, Containers)

Guided discovery of the TensorRT-LLM 2026 environment through quick installation on NVIDIA GPU, creation of optimized Docker containers, hands-on with build tools to convert a PyTorch LLM into a TensorRT engine, practical exercises on Llama or Mistral with basic quantization, generation of first accelerated tokens, performance comparison before/after, and production of an initial evaluation report to validate your professional skills.

Module 2TensorRT-LLM Optimization: Performance Tuning and Kernels (FlashAttention, Paged KV Cache)

Deep dive into TensorRT-LLM optimization techniques such as enabling FlashAttention-2 for reduced latency, configuring paged attention KV cache for large contexts, experimentation with multi-GPU using tensor parallelism, exercises on tuning batch sizes and maximum throughput, real enterprise cases with open-source models, precise measurement using NVIDIA Nsight, and development of a personalized optimization plan to boost your inferences up to 5x faster.

Module 3TensorRT-LLM Deployment: Production Integration and Monitoring (Triton Inference Server, Secure APIs)

Mastery of TensorRT-LLM deployment via Triton Inference Server for scalable APIs, integration with FastAPI or gRPC for enterprise services, securing endpoints with authentication, real-time GPU metrics monitoring via DCGM and Prometheus, exercises on horizontal scaling, resolution of real production bottlenecks, load testing with Locust, and finalization of a deployable ongoing project with complete documentation for immediate enterprise implementation.