Program profile · official sources
Fast & Efficient LLM Inference with vLLM
This short course from DeepLearning.AI and Red Hat follows an optimize, deploy and benchmark workflow for an open-source language model. Learners quantize a model with LLM Compressor, serve it with vLLM, and benchmark performance and quality using GuideLLM and lm-eval. The course covers GPU memory, continuous batching, PagedAttention and prefix caching. Python and basic LLM familiarity are recommended.
Facts checked 15 Oct 2026
- Price
- See pricing
As shown in the United States on 15 October 2026, the page does not display a numeric fee. Embedded metadata categorizes an offer as Free, while visible copy associates accomplishments and graded features with Pro. The scope of that embedded free offer is not established; no free credential or numeric enrollment price is inferred.
- Provider link
See price and enroll at DeepLearning.AI (official site) ↗
Official site. No commission.
- Time
- 1h38m listed course duration; individual activity estimates are rounded
- Level
- Intermediate
- Credential
- Accomplishment with Pro
- Prerequisites
- Python and basic LLM concepts, including tokens, inference and GPU usage, are recommended.
- Format
- Online course with video lessons and listed learning activities
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
Sources ↗
No accepted numeric price in this registry. See the sourced pricing details below.
See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.
What’s covered
- Introduction
- Why Efficient LLM Deployment Matters
- Inference & Memory Fundamentals
- LLM Optimization Fundamentals
- Optimizing a Model with LLM Compressor
- Serving LLMs Efficiently with vLLM - Part I
- Serving LLMs Efficiently with vLLM - Part II
- Measuring What Matters: Benchmarking and Evaluation
- Conclusion: Putting it All Together
- Quiz
Prerequisites: Python and basic LLM concepts, including tokens, inference and GPU usage, are recommended.
What you actually do
- Introduction : 3m; Video; provider outline item, not a separately named module · Source
- Why Efficient LLM Deployment Matters : 6m; Video; provider outline item, not a separately named module · Source
- Inference & Memory Fundamentals : 14m; Video; provider outline item, not a separately named module · Source
- LLM Optimization Fundamentals : 14m; Video; provider outline item, not a separately named module · Source
- Optimizing a Model with LLM Compressor : 10m; Video with Code Example; provider outline item, not a separately named module · Source
- Serving LLMs Efficiently with vLLM - Part I : 10m; Video; provider outline item, not a separately named module · Source
- Serving LLMs Efficiently with vLLM - Part II : 7m; Video with Code Example; provider outline item, not a separately named module · Source
- Measuring What Matters: Benchmarking and Evaluation : 15m; Video with Code Example; provider outline item, not a separately named module · Source
- Conclusion: Putting it All Together : 4m; Video; provider outline item, not a separately named module · Source
- Quiz : 10m; Graded; provider outline item, not a separately named module · Source
What the credential is
- Credential type
- Accomplishment with Pro · www.deeplearning.ai / fast-and-efficient-llm-inference-with-vllm
- Credential
- Accomplishment with Pro · www.deeplearning.ai / fast-and-efficient-llm-inference-with-vllm
- Exam requirement
- A graded quiz is listed; its passing threshold and any separate final examination are not stated. · www.deeplearning.ai / fast-and-efficient-llm-inference-with-vllm
- Credential scope
- As checked on 15 October 2026, the course page identifies an accomplishment with Pro, not an explicitly named academic or professional certification. The capture does not establish an independent credential issuer, full completion thresholds, expiry, renewal or academic-credit eligibility. · www.deeplearning.ai / fast-and-efficient-llm-inference-with-vllm
Source differences retained
As checked on 15 October 2026, the official page gives differing information on the points below. Each reading is kept as found; none is reconciled.
- Duration: Visible course duration is 1h38m; same-course embedded metadata has generic courseWorkload PT1H. The visible duration is retained as text; no single numeric completion estimate is used. · Source
- Language: As checked on 15 October 2026, the same-course structured metadata states inLanguage="en" and availableLanguage=["en","jp","kr"]. English is retained; the additional literal codes do not establish whether they describe teaching audio, subtitles or another language option. No additional delivery language is verified. · Source
Alternatives in the registry
Related by topic or level.
- Transformers in Practice : Check official price; 11h1m listed course duration; individual activity estimates are rounded (duration checked 15 October 2026).
- Finetuning with Llama : Check official price; 2 hours (provider headline; section and exam times listed separately) (duration checked 25 September 2026).
- Introduction to LLaMA : Check official price; 2 hours (provider headline; module and exam timings listed separately) (duration checked 27 September 2026).
Change log
- : Initial source review from the dated US capture. · Source
See price and enroll at DeepLearning.AI (official site) ↗
Official site. No commission.