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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training vLLM 2026 - Accelerate LLM Inference x10 in Production training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training vLLM 2026 - Accelerate LLM Inference x10 in Production training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training vLLM 2026 - Accelerate LLM Inference x10 in Production training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Discover the quick installation of vLLM 2026 on GPU clusters, configure environments with PyTorch 2.x and CUDA 12, integrate Docker for maximum portability, perform practical exercises on models like Llama 3, test first inference servers with variable loads, produce initial benchmarks and optimized configuration reports, while exploring 2026 specifics for native x5 acceleration.
Dive into advanced optimization via PagedAttention and AWQ/GPTQ quantization, implement continuous batching for x10 TPS, deploy in Kubernetes with auto-scaling, integrate Prometheus for real-time monitoring, analyze real enterprise cases like scalable chatbots, resolve complex troubleshooting scenarios, generate production-ready deliverables, strengthening your certified professional AI skills.
Target audience
Data scientists, ML engineers, AI architects, and experienced DevOps professionals aiming to upskill in optimized LLM inference in enterprise settings
Prerequisites
Advanced mastery of Python and PyTorch, experience in LLM deployment, knowledge of CUDA and Docker containers, basics in GPU optimization
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