vLLM and DeepLearning.AI launch a course on fast LLM inference
On 3 June 2026, the vLLM Blog announced that the vLLM team, in collaboration with DeepLearning.AI, is launching a free course on optimization, deployment and benchmarking of LLM inference. The program covers quantization via the LLM Compressor tool, the GuideLLM tool, KV cache sizing, and serving and memory trade-offs.
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The vLLM team, in collaboration with the DeepLearning.AI platform, is launching a free course on LLM inference, as announced on the vLLM Blog on 3 June 2026. The course focuses on practical skills for optimization, deployment and benchmarking of inference for large language models, an area that is becoming increasingly important as models enter real production systems.
Who is behind the course?
The course is jointly organized by the vLLM team and DeepLearning.AI. vLLM is a popular open framework for fast and memory-efficient inference of large language models, known for techniques such as PagedAttention that optimize memory usage. DeepLearning.AI is an educational platform founded by Andrew Ng and known for accessible courses in the field of artificial intelligence.
The combination of a framework used in production and an educational platform with a wide reach means the course targets practitioners who want to apply the knowledge directly in their systems.
What does the course cover?
The program covers three major topics: optimization, deployment and benchmarking of LLM inference. Specifically, it addresses quantization via the LLM Compressor tool. Quantization is a technique for reducing the precision of numbers in a model to save memory and speed it up, and LLM Compressor is a tool that automates that process.
The course also introduces the GuideLLM tool, KV cache sizing (the buffer that holds computed values during text generation), and serving and memory trade-offs. The KV cache directly affects how many concurrent requests a model can handle, so its correct sizing is crucial for efficient serving.
Who is the course intended for?
The course is intended for engineers and researchers who want to learn how to serve large language models quickly, cheaply and reliably. Understanding the serving and memory trade-offs helps teams make informed decisions about how to allocate resources between speed, cost and quality.
It is important to emphasize that this is a promotional and educational publication. The announcement does not provide hard benchmark performance numbers, but rather announces educational content. Concrete details about the schedule and enrollment are available at the primary source, the vLLM Blog, and on the DeepLearning.AI platform.
Frequently Asked Questions
- Who is organizing the course on LLM inference?
- The course is jointly organized by the vLLM team and the DeepLearning.AI platform. vLLM is a popular framework for fast inference of large language models, and DeepLearning.AI is an educational platform known for courses in the field of artificial intelligence.
- What does the course cover?
- The course covers optimization, deployment and benchmarking of LLM inference. It addresses quantization via the LLM Compressor tool, the GuideLLM tool, KV cache sizing, and serving and memory trade-offs. The goal is to understand how to serve language models quickly and efficiently.
- Is the course free?
- Yes, according to the announcement on the vLLM Blog of 3 June 2026, the course is free. It is an educational publication without hard benchmark numbers, aimed at teaching practical skills for optimizing inference.