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Training TensorRT-LLM - Accelerate LLM Inference x10 in Production

Ref: ICW981
10 people max.
5500€ 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
5 journées
distanciel

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Learning objectives

  • Master TensorRT-LLM to optimize LLM inference in enterprise settings
  • Develop professional skills in converting and building high-performance TensorRT engines
  • Design scalable inference pipelines with dynamic batching and paged attention
  • Implement advanced quantization techniques and kernel fusion for x10 gains
  • Optimize performance on H100/A100 in Qualiopi-certified environments
  • Deploy production-ready solutions with integrated monitoring and CI/CD

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.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1TensorRT-LLM Fundamentals: Installation and Engine Build (TensorRT-LLM, CUDA 12, Triton)

Discover the complete installation of TensorRT-LLM on NVIDIA clusters, configure Docker and CUDA Toolkit dependencies, perform your first engine build from a Hugging Face model like Llama-2, test basic inference with latency/throughput benchmarks, produce initial performance reports, apply practical exercises on A100 GPUs to validate professional setups.

Module 2TensorRT-LLM Model Conversion: PyTorch/HF to Optimized Engines (LoRA, GPTQ)

Convert LLMs like Mistral or GPT-J from PyTorch to TensorRT format via TRT-LLM scripts, integrate LoRA adaptations for efficient fine-tuning, apply FP8/INT4 quantization to reduce memory x4, generate and validate engines with NVIDIA Nsight profiling, simulate enterprise cases on real datasets, obtain deployable deliverables with immediate throughput gains.

Module 3Advanced TensorRT-LLM Optimizations: In-Flight Batching, Paged Attention (KV Cache)

Implement in-flight batching to handle variable requests in real-time, enable paged attention for LLMs >70B without OOM, fuse custom CUDA kernels to accelerate matmuls, tune hyperparameters via TRT-LLM auto-tuning, measure impacts on H100 with DCGM tools, practice on scalable chatbot workloads, produce production-ready optimized configs.

Module 4TensorRT-LLM Deployment: Multi-GPU Scaling, Triton Inference Server Integration

Deploy TensorRT-LLM engines on Kubernetes with Triton Server for auto-scaling, configure multi-GPU tensor parallelism and pipeline parallelism, integrate Prometheus/Grafana monitoring for latencies <50ms, test high availability with fault-tolerance, simulate enterprise traffic 1000+ req/s, generate CI/CD pipelines with GitHub Actions, validate robustness in real conditions.

Module 5Real-World TensorRT-LLM Projects: Performance Optimization, Certification, and Enterprise Cases

Carry out a capstone project optimizing a custom LLM for RAG in production, analyze bottlenecks with Nsight Compute, apply multi-query attention techniques, benchmark against vLLM to prove x3-10 superiority, prepare skills certification via quizzes and portfolio, discuss enterprise ROI feedback, receive post-training support for live deployments.

Evaluation method

  • Daily technical quizzes on TensorRT-LLM concepts
  • Practical case studies with measured performance metrics
  • Final project deployed and presented to an expert panel

Learning method

  • Active methods: 70% hands-on labs on dedicated GPUs
  • Individualized support in small groups of 10 max
  • Unlimited resources: codes, docs, video replays
  • Qualiopi certification validating expert competencies

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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