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Training vLLM 2026 - Deploying Ultra-High-Performance LLMs

Ref: OWA207
10 people max.
7000€ 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 vLLM 2026 to accelerate inference of professional LLMs
  • Develop scalable and optimized inference servers
  • Implement advanced PagedAttention to handle long contexts
  • Design multi-model pipelines with real-time monitoring
  • Optimize costs and latency for enterprise deployments
  • Deploy vLLM 2026 in certified Kubernetes clusters
  • Acquire expert skills in high-performance AI serving

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 1vLLM 2026 Fundamentals: Advanced Installation and Configuration (CUDA 12+, Parallel Tensors)

Dive into expert installation of vLLM 2026 on multi-node GPU environments, configure advanced parameters for PyTorch 2.4 and TensorRT-LLM, test compatibility with Llama 3 and Mistral, perform initial benchmarks on professional datasets, produce a baseline performance report, and create a Docker automation script for rapid enterprise deployment.

Module 2vLLM 2026 Optimization: PagedAttention and Custom Kernels (AWQ/GPTQ Quantization)

Explore the deep mechanisms of PagedAttention for ultra-long contexts up to 1M tokens, implement custom kernels for x5 acceleration, apply 4-bit quantization on giant models like GPT-4o, measure throughput gains through real-load exercises, generate optimized configurations, and produce a reusable automated tuning deliverable for production.

Module 3vLLM 2026 Scaling: Multi-Models and Distribution (Ray, Tensor Parallelism)

Build multi-tenant servers with vLLM 2026 handling 10+ simultaneous models, deploy Tensor and Pipeline Parallelism on GPU clusters, integrate Ray for dynamic auto-scaling, simulate enterprise traffic spikes with 1000 RPS, analyze bottlenecks via integrated profiling, and produce a scalable Kubernetes blueprint and custom metrics dashboard.

Module 4vLLM 2026 Integration: OpenAI-Compatible APIs and Monitoring (Prometheus, Grafana)

Develop OpenAI-compatible REST/gRPC endpoints with vLLM 2026, secure with JWT authentication and rate-limiting, integrate Prometheus for real-time observability, test failover and load-balancing in fault-tolerant scenarios, add structured ELK logging, and generate a complete API gateway and monitoring playbook for proactive enterprise alerting.

Module 5Production Deployment of vLLM 2026: CI/CD and Expert Cases (Dynamic Finetuning)

Master CI/CD pipelines with GitHub Actions for vLLM 2026 in Helm/K8s, implement zero-downtime rolling updates, test online LoRA finetuning on live serving, resolve edge cases like OOM and model drift, validate with OWASP security audit, and produce a deployed and certified red thread project ready for immediate ROI in production.

Evaluation method

  • Expert MCQ on vLLM 2026 architectures and optimizations
  • Practical evaluation via throughput and latency benchmarks
  • Defense of the red thread project for multi-model serving

Learning method

  • Courses led by vLLM production experts
  • Practical exercises on real enterprise GPU clusters
  • Scalable red thread project throughout the training
  • Detailed course materials and source codes provided

Methods, materials and delivery

The Training vLLM 2026 - Deploying Ultra-High-Performance LLMs 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 vLLM 2026 - Deploying Ultra-High-Performance LLMs 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 vLLM 2026 - Deploying Ultra-High-Performance LLMs 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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