Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson
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Elizabeth Goodman
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Elizabeth Goodman
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Terms in this piece · Glossary
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.
speculative decoding — A speed trick where a small model drafts several tokens ahead and the big model verifies them in one pass, often doubling generation speed.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
Shows NVFP4 quantizationShrinking a model by storing its numbers less precisely — like rounding — so it runs faster and fits on smaller hardware, at a small quality cost.Full definition → combined with speculative decodingA speed trick where a small model drafts several tokens ahead and the big model verifies them in one pass, often doubling generation speed.Full definition → can speed on-device reasoning-model inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → by up to 6.28x on Jetson, with the optimal decoding method differing per model, concrete guidance for deploying agentic models at the edge.
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