• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
• quantization — Shrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.
• KV cache — The memory a model keeps about text it has already read, so generating each new token doesn't require reprocessing the whole conversation.

NVIDIA's TensorRT Edge-LLM ran Qwen3.6-27B on a single Jetson AGX Thor at 52 tokens/sec, finishing MLPerf's Edge Agentic benchmark 6.4x faster than llama.cpp using NVFP4 quantization, FP8 KV cache and tree-based multi-token prediction.

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