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jetson-inference-mem-tune

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Pick the serving stack and per-runtime memory flags (vLLM, SGLang, llama.cpp, TensorRT Edge-LLM) for an LLM/VLM workload on any NVIDIA Jetson.

skills/jetson-inference-mem-tune/SKILL.md

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# Jetson Inference Memory Tuning

Recommends an inference runtime and the specific memory-related flags to pass to it, given the Jetson SKU/variant and the user's workload. Does not include quantization recipe selection — that lives in the model-benchmarking skill — but it does point at the precision floor each runtime can serve efficiently.

## Purpose

Turn a live `jetson-memory-audit` snapshot into runtime and launch-flag recommendations for LLM/VLM serving on Jetson. Use this when the user needs to fit a model, reduce OOM risk, or switch to a lower-memory serving stack.

## When to use

- "Which serving stack should I use on Orin Nano 8 GB to run a 7B model?"
- "vLLM is OOMing — what should `--gpu-memory-utilization` and `--max-model-len` be?"
- "Same model, less memory — can I switch from vLLM to llama.cpp?"
- After `jetson-memory-audit` shows a model server is the top NvMap / PSS consumer.
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