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Training TensorRT-LLM 2026 - Accelerate LLM Inference in Production

Ref: HJA425
10 people max.
4400€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
4 journées
distanciel

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Learning objectives

  • Master the installation and initial configuration of TensorRT-LLM 2026 in certified professional training.
  • Develop skills to optimize LLM inference using advanced NVIDIA tools.
  • Design fast inference pipelines tailored to enterprise needs.
  • Implement quantization and layer fusion techniques to boost performance.
  • Deploy TensorRT-LLM 2026 models in production with effective monitoring.
  • Acquire certified expertise in prompt engineering for optimized LLMs.
  • Optimize GPU resources to reduce costs in professional environments.

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

Don't let this gap widen

Why this program matters

  • Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Introduction to TensorRT-LLM 2026: Installation and First Models (NVIDIA Tools, Python, Docker Containers)

Discover the foundations of TensorRT-LLM 2026 through guided installation on GPU environments, configure Docker and essential dependencies, test your first LLM models like Llama with simple Python scripts, perform basic inference practical exercises, analyze initial performance metrics, and produce a diagnostic report to identify potential gains, while integrating real enterprise cases for immediate application.

Module 2TensorRT-LLM 2026 Configuration: Inference Pipelines and Basic Optimization (TRT Engines, Dynamic Batching)

Dive into creating optimized TensorRT engines for TensorRT-LLM 2026, configure dynamic batching and KV cache to accelerate long sequences, experiment with GPT and Mistral models on real datasets, measure latencies before/after optimization using TensorBoard, resolve common pitfalls in groups, and generate production-ready configurations, with a focus on enterprise integration and downloadable deliverables.

Module 3Advanced Optimization in TensorRT-LLM 2026: Quantization and Layer Fusion (FP8, Multi-GPU)

Master FP8 and INT4 quantization techniques in TensorRT-LLM 2026 to halve memory requirements, fuse attention layers for 30% speed gains, deploy on multi-GPU setups with Triton Inference Server, test on standard benchmarks like MLPerf, debug using NVIDIA logs and profiling tools, apply to concrete business cases like scalable chatbots, and export optimized models with quantified metrics for your professional portfolio.

Module 4TensorRT-LLM 2026 Deployment: Production and Monitoring (Kubernetes, Observability)

Finalize deployment of TensorRT-LLM 2026 in Kubernetes containers for high availability, integrate Prometheus and Grafana for real-time monitoring of latencies and throughput, simulate production loads with Locust, secure API endpoints via Helm charts, optimize for edge computing if relevant, review the entire program in a personal capstone project, and receive a certificate with complete deliverables to showcase your enterprise skills.

Evaluation method

  • Daily interactive quizzes on TensorRT-LLM 2026 concepts to validate theoretical knowledge.
  • Practical case studies with expert feedback on real-world optimizations.
  • Final project deploying an optimized LLM model, graded and certified.

Learning method

  • 70% hands-on practice with exercises on NVIDIA GPUs in interactive remote format.
  • Real-world use cases from leading generative AI companies.
  • Unlimited video support and post-training resources for 6 months.
  • Live Q&A sessions with certified NVIDIA trainers for immediate resolution.

Methods, materials and delivery

The Training TensorRT-LLM 2026 - Accelerate LLM Inference in Production program is delivered onsite or remote (blended-learning, e-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training TensorRT-LLM 2026 - Accelerate LLM Inference in Production program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training TensorRT-LLM 2026 - Accelerate LLM Inference in Production program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
Our method

Training quality, guaranteed at every step

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

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