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Training vLLM - Deploying Ultra-Fast LLMs in Production

Ref: TER314
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 the vLLM architecture to accelerate LLM inference in certified professional training
  • Develop skills in PagedAttention to optimize GPU memory in enterprise contexts
  • Design scalable inference pipelines with vLLM tailored to professional needs
  • Implement OpenAI-compatible deployments via vLLM for high-performance APIs
  • Optimize vLLM performance in production, reducing costs and latency for the enterprise
  • Evaluate and tune LLMs with vLLM, strengthening certified AI 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.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1vLLM Fundamentals: Installation and Architecture (PyTorch tools, Docker, first servers)

Discover rapid vLLM installation via pip and Docker, configure first LLM inference servers like Llama or Mistral, explore the revolutionary PagedAttention architecture for KV-cache management, perform practical benchmarking exercises on NVIDIA GPUs, generate your first batched tokens, and produce an initial performance report to validate throughput gains.

Module 2vLLM Memory Optimization: PagedAttention and Tuning (KV-cache methods, prefix caching)

Dive into PagedAttention for up to 4x better memory utilization, implement prefix caching and chunked prefill for long sequences, test on real datasets like ShareGPT, adjust hyperparameters via vLLM CLI, measure impacts on latency and TPS, and deploy a customized server with detailed logging for real-time monitoring.

Module 3vLLM API Deployment: OpenAI Compatibility and Scaling (FastAPI, Kubernetes, load balancing)

Integrate vLLM with the OpenAI API for seamless migration, build custom endpoints with FastAPI and JWT authentication, scale horizontally via Kubernetes and Ray Serve, simulate high loads with Locust, optimize multi-GPU with tensor parallelism, and generate Prometheus dashboards to visualize critical metrics like tokens/sec.

Module 4Advanced vLLM: Multi-LoRA and Speculation (LoRAX tools, lookahead decoding, distillation)

Master multi-LoRA serving with LoRAX for thousands of adapters without downtime, implement assisted-generation and lookahead decoding to boost speed up to 2x, experiment with quantized AWQ/GPTQ models, integrate distillation techniques for edge deployment, test in advanced RAG scenarios, and produce a complete production-ready enterprise pipeline.

Module 5vLLM in Production and Monitoring: Security, Observability, and Use Cases (Prometheus, Grafana, CI/CD)

Secure vLLM deployments with rate limiting and input validation, configure observability via Prometheus and Grafana for real-time alerts, integrate with CI/CD pipelines using GitHub Actions, analyze real cases from enterprises like Hugging Face users, optimize AWS/GCP cloud costs, and finalize a capstone project with full-stack deployment and certification report.

Evaluation method

  • Daily technical quizzes on vLLM and benchmarks
  • Practical project for full inference server deployment
  • Enterprise case study with oral presentation and certification

Learning method

  • Active pedagogy with 70% hands-on practice on cloud GPU environments
  • Individualized hands-on exercises in small groups of max 10
  • Real enterprise cases using vLLM in AI production
  • 3-month post-training support with expert coaching and alumni community

Methods, materials and delivery

The Training vLLM - Deploying Ultra-Fast LLMs 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 vLLM - Deploying Ultra-Fast LLMs 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 vLLM - Deploying Ultra-Fast LLMs 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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