vLLM internals deep dive video
A new deep-dive YouTube explainer walks the vLLM inference pipeline — covering token streaming, KV cache management, async scheduling and GPU-native sampling changes that matter for throughput and debugging. The video is a practical resource for engineers wrestling with vLLM vs. TensorRT-LLM trade-offs in shared clusters. (youtube.com)
KodeKloud published the walkthrough titled "Understanding vLLM with a Hands On Demo" on March 31, 2026 and the video runtime is roughly 14 minutes per the YouTube description. (youtube.com) The video’s description includes a sequence of hands-on tasks — naive HuggingFace prefill, an offline vLLM run, a PagedAttention demo, launching an OpenAI-compatible API server, and building a live monitoring dashboard — with timestamps listed in the description. (youtube.com) vLLM’s public engineering notes and blog call out PagedAttention, continuous batching, prefix caching and speculative decoding as core subsystems that enable its memory and scheduling behavior, citing the original paged-attention paper and implementation details. (vllm.ai) The vLLM project traces to the Sky Computing Lab at UC Berkeley and is maintained as an open community project on GitHub with active docs, release notes and configuration knobs for gpu_memory_utilization and chunked-prefill. (github.com) Independent benchmarks show a clear trade-off: an RTX 4090 test reported TensorRT-LLM at ~89 tokens/sec versus vLLM at ~38 tokens/sec for Llama 3.1 8B (≈2.3× higher throughput for TensorRT-LLM under that homogeneous-batch test). (tildalice.io) On H100 FP8 tests with Llama 3.3 70B, a side-by-side run measured vLLM at ~1,850 tok/s and TensorRT-LLM at ~2,100 tok/s, while also reporting cold-start/model-compilation differences (vLLM cold start ≈62s vs TensorRT-LLM cold start ≈28 minutes in that study). (spheron.network) The KodeKloud demo bundles a free hands-on lab link and prepared environment for following the exact steps shown in the video, including the API launch and dashboard capstone referenced in the description. (youtube.com)
