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Training vLLM 2026 - Deploying Scalable LLM Inferences

Ref: OWL354
10 people max.
5600€ 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 vLLM 2026 for ultra-fast LLM inferences in enterprise settings
  • Develop scalable vLLM servers with advanced PagedAttention
  • Optimize multi-node GPU resources in professional environments
  • Implement certified vLLM pipelines for high-load APIs
  • Design secure and monitored vLLM deployments in production
  • Deploy vLLM 2026 hybrid cloud for enterprise-grade skills

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 1vLLM 2026 Architecture: PagedAttention and Expert Configuration (Quantization, Tensor Parallelism)

Dive into advanced installation of vLLM 2026 on GPU clusters, configure PagedAttention to minimize KV-cache memory during long inferences, test AWQ/GPTQ quantization on Llama 405B models, perform practical benchmarking exercises for throughput/latency, produce customized performance reports, apply real business cases to validate immediate efficiency gains.

Module 2vLLM 2026 Scaling: Multi-GPU and Distributed Inference (Ray, Kubernetes Integration)

Deploy vLLM 2026 in distributed mode on 8+ NVIDIA H100 GPUs, integrate Ray for dynamic worker orchestration, simulate production loads with 1000+ requests/s, optimize tensor and pipeline parallelism through hands-on exercises, monitor via Prometheus/Grafana in real-time, generate scalable deliverables ready for enterprise APIs, transform your skills into concrete business assets.

Module 3vLLM 2026 API and Security: OpenAI-Compatible Endpoints (Authentication, Rate Limiting)

Build OpenAI-compatible endpoints with vLLM 2026, implement JWT/OAuth for advanced security, configure adaptive rate limiting against DDoS abuse, test chat completions and embeddings on real enterprise datasets, integrate structured logging with ELK stack, develop custom middleware for inference fine-tuning, produce production-ready certified APIs, boost your professional expertise.

Module 4vLLM 2026 Production: Monitoring, Tuning, and Hybrid Cloud (AWS SageMaker, GCP Vertex)

Optimize vLLM 2026 in production with Kubernetes auto-scaling, deploy on AWS/GCP hybrid via Terraform exercises, tune hyperparameters for RoPE scaling on 1M+ contexts, monitor GPU costs live with CloudWatch, simulate failures and HA recovery, finalize red thread project with custom dashboard, acquire certifying skills for AI leadership in enterprise.

Evaluation method

  • Expert multiple-choice quiz to validate vLLM 2026 skills at end of training
  • Continuous evaluation through scaling and GPU optimization exercises
  • Defense of red thread project for production vLLM deployment

Learning method

  • Courses by vLLM expert trainer active in AI production
  • Practical exercises with real enterprise LLM cases using vLLM 2026
  • Red thread scalable project over 4 days of expert training
  • Complete course support with updated vLLM resources

Methods, materials and delivery

The Training vLLM 2026 - Deploying Scalable LLM Inferences 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 Scalable LLM Inferences 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 Scalable LLM Inferences 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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