Uploaded August 2026 | Updated September 2026, 1 day ago
Inference engines are full of jargon — continuous batching, paged attention, prefix caching, speculative decoding — and if you've ever nodded along while understanding very little, this video is for you. In one focused pass, it breaks down the 12 core ideas behind how language models actually run in production: what an engine does, what it's holding on that GPU, and exactly how each optimization works under the hood.
More importantly, the video answers the question that actually matters: *when* does each of these things start to matter to *you*? Some bite the moment you deploy anything at all. Some wait until fifty people are using your service simultaneously. And some, honestly, you may never need. Rather than handing you a list to memorize, this is a practical framework for figuring out where you are right now — and what to focus on next.
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Sponsor: Infisical
🔗 infisical.com/?utm_source=youtube&utm_medium=paid&utm_campaign=devops_toolkit_review_ai_written_code
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#LLMInference #AIEngineering #MachineLearning
Consider joining the channel: youtube.com/c/devopstoolkit/join
▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬
➡ Transcript and commands: https://devopstoolkit.live/ai/llm-inference-explained-12-concepts-you-actually-need-to-know
▬▬▬▬▬▬ 💰 Sponsorships 💰 ▬▬▬▬▬▬
If you are interested in sponsoring this channel, please visit https://devopstoolkit.live/sponsor for more information. Alternatively, feel free to contact me over Twitter or LinkedIn (see below).
▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬
➡ BlueSky: https://vfarcic.bsky.social
➡ LinkedIn: linkedin.com/in/viktorfarcic
▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬
🎤 Podcast: devopsparadox.com
💬 Live streams: youtube.com/c/DevOpsParadox
▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬
00:00 LLM Inference Jargon, Decoded
01:10 Sponsor (Infisical)
02:20 What An Inference Engine Does (vLLM, Ollama, etc.)
05:15 GPU Memory: Model Weights vs KV Cache
07:39 Continuous Batching And Paged Attention
09:47 Quantization: More KV Cache, Same GPU
10:58 Prefix Caching: The Big Lever For Agents
12:28 Prefill vs Decode, And Speculative Decoding
14:48 Control Planes And LLM Gateways
16:30 Sharding, Autoscaling, And Cold Starts
18:38 When Each One Starts To Matter
Inference engines are full of jargon — continuous batching, paged attention, prefix caching, speculative decoding — and if you've ever nodded along while understanding very little, this video is for you. In one focused pass, it breaks down the 12 core ideas behind how language models actually run in production: what an engine does, what it's holding on that GPU, and exactly how each optimization works under the hood.
More importantly, the video answers the question that actually matters: *when* does each of these things start to matter to *you*? Some bite the moment you deploy anything at all. Some wait until fifty people are using your service simultaneously. And some, honestly, you may never need. Rather than handing you a list to memorize, this is a practical framework for figuring out where you are right now — and what to focus on next.
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Sponsor: Infisical
🔗 infisical.com/?utm_source=youtube&utm_medium=paid&utm_campaign=devops_toolkit_review_ai_written_code
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
#LLMInference #AIEngineering #MachineLearning
Consider joining the channel: youtube.com/c/devopstoolkit/join
▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬
➡ Transcript and commands: https://devopstoolkit.live/ai/llm-inference-explained-12-concepts-you-actually-need-to-know
▬▬▬▬▬▬ 💰 Sponsorships 💰 ▬▬▬▬▬▬
If you are interested in sponsoring this channel, please visit https://devopstoolkit.live/sponsor for more information. Alternatively, feel free to contact me over Twitter or LinkedIn (see below).
▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬
➡ BlueSky: https://vfarcic.bsky.social
➡ LinkedIn: linkedin.com/in/viktorfarcic
▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬
🎤 Podcast: devopsparadox.com
💬 Live streams: youtube.com/c/DevOpsParadox
▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬
00:00 LLM Inference Jargon, Decoded
01:10 Sponsor (Infisical)
02:20 What An Inference Engine Does (vLLM, Ollama, etc.)
05:15 GPU Memory: Model Weights vs KV Cache
07:39 Continuous Batching And Paged Attention
09:47 Quantization: More KV Cache, Same GPU
10:58 Prefix Caching: The Big Lever For Agents
12:28 Prefill vs Decode, And Speculative Decoding
14:48 Control Planes And LLM Gateways
16:30 Sharding, Autoscaling, And Cold Starts
18:38 When Each One Starts To Matter
