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Training TensorRT-LLM - Accelerate LLM Inference x10 in Production

Ref: RXM311
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 configuration of TensorRT-LLM for certified professional environments
  • Develop skills in compiling and optimizing LLMs for ultra-fast inference
  • Design scalable inference pipelines tailored to enterprise AI needs
  • Implement advanced quantization techniques and kernel fusion with TensorRT-LLM
  • Deploy high-performance TensorRT-LLM servers in production to reduce GPU costs
  • Optimize LLM performance by integrating professional benchmarks and real-world cases

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 1TensorRT-LLM Fundamentals: Installation, Engine Building, and Initial Tests (NVIDIA Tools, PyTorch, CUDA)

Discover the quick installation of TensorRT-LLM on GPU clusters, configure essential dependencies with conda and Docker, build your first TensorRT engines from Hugging Face models like Llama or GPT, test basic inference via Python backend, analyze performance logs to identify bottlenecks, and produce an initial speedup report, all through guided practical exercises to anchor professional skills from the first day.

Module 2Advanced TensorRT-LLM Optimizations: Quantization, Kernel Fusion, and Tuning (TensorRT Plugins, FP8, AWQ)

Dive into INT4/INT8/FP8 quantization techniques to reduce GPU memory by up to 4x, fuse attention and MLP kernels with custom TensorRT-LLM plugins, tune hyperparameters via automated scripts, benchmark on A100/H100 with Triton Inference Server, implement AWQ for 70B parameter LLMs, generate custom latency profiles, and validate gains through real exercises on professional datasets, boosting your enterprise optimization skills.

Module 3TensorRT-LLM Deployment: Scalable Serving and Pipeline Integration (Triton, Kubernetes, gRPC API)

Deploy TensorRT-LLM backends with Triton Server for handling thousands of concurrent requests, integrate into Kubernetes for GPU auto-scaling, expose optimized REST/gRPC APIs, manage KV-cache paging for long contexts, monitor with Prometheus and Grafana, test resilience under load with Locust, produce production-ready Docker images, and simulate enterprise scenarios in practical exercises, ensuring certified mastery of high-availability deployment.

Module 4Advanced TensorRT-LLM Cases: Benchmarks, Troubleshooting, and Real Projects (Multi-GPU, MoE, Custom Ops)

Refine multi-GPU performance with tensor parallelism and pipeline parallelism, implement Mixture of Experts on TensorRT-LLM, debug memory leaks and overflows using NVIDIA Nsight, customize ops for specific domains like finance or healthcare, benchmark against vLLM and Hugging Face TGI, finalize a personal capstone project with source code deliverable and performance report, and prepare for certification through peer review, consolidating expert skills for the enterprise.

Evaluation method

  • Technical quizzes on TensorRT-LLM optimizations and benchmarks
  • Practical project for deploying scalable inference servers
  • Real case study with performance analysis and report

Learning method

  • Practical methods with 70% hands-on exercises on NVIDIA GPUs
  • Unlimited access to virtual labs and real LLM datasets post-training
  • Video replays and PDF materials for self-review
  • Qualiopi certification validating expert inference skills

Methods, materials and delivery

The Training TensorRT-LLM - Accelerate LLM Inference x10 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 - Accelerate LLM Inference x10 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 - Accelerate LLM Inference x10 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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