# jetson-inference-mem-tune

Picks the serving stack and memory flags for an LLM or VLM on NVIDIA Jetson. Use it when a model won't fit or the server keeps hitting OOM.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1200 installs · ★ 3735
- Author: Jetson Team
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-inference-mem-tune/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/jetson-inference-mem-tune

## What it does

The skill reads a JSON snapshot from `jetson-memory-audit` and returns a runtime plus the exact flags to launch it with. Four stacks are covered: llama.cpp, vLLM, SGLang and TensorRT Edge-LLM. You get concrete values such as `--gpu-memory-utilization=0.55` or `-ngl 28`, along with an alternative runtime and the reasoning behind it.

There is one script, `scripts/recommend.py`. It takes the audit path, the workload type (`llm-server`, `vlm-server`, `embedding`, `rag`) and an optional `--target-mb` free-memory goal. Without that goal it falls back to SKU defaults. Invoke it through `run_script` if your agent supports that, otherwise run `python3` from the repository root.

Recommendations are only as fresh as the audit. Stop a service, change the power mode, restart a model server — and the snapshot is stale, so re-run the audit. Memory pressure is estimated from SKU defaults and audit totals; KV-cache, quantization and tokenizer behaviour for a specific model can still call for benchmarking. The skill only prints flags. It never starts or restarts servers. If `launch_flags` comes back empty, don't invent your own — that means the script failed.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-inference-mem-tune -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/jetson-inference-mem-tune/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/jetson-inference-mem-tune/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-inference-mem-tune -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/jetson-inference-mem-tune/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-inference-mem-tune -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/jetson-inference-mem-tune/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills jetson-inference-mem-tune
```

2. **Manually: put the files in place**
- File: `.github/skills/jetson-inference-mem-tune/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-inference-mem-tune -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/jetson-inference-mem-tune
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/jetson-inference-mem-tune/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill jetson-inference-mem-tune -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/jetson-inference-mem-tune/SKILL.md

## When to use it
- fit a 7B model on an Orin Nano 8 GB
- pull a vLLM server out of OOM with tighter limits
- switch from vLLM to llama.cpp to cut memory use
- derive launch flags from a fresh memory snapshot
- choose a stack for RAG or embedding workloads on Jetson

## Example prompts
- vLLM keeps OOMing — what should --gpu-memory-utilization and --max-model-len be?
- Same model, less memory: can I switch from vLLM to llama.cpp?
- Which serving stack should I use on Orin Nano 8 GB for a 7B model?

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 31a923e1c08ce02f.

BEGIN THIRD-PARTY FILE 31a923e1c08ce02f
```text
---
name: jetson-inference-mem-tune
description: 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.
version: 0.0.1
license: "Apache-2.0"
metadata:
  author: "Jetson Team"
  tags: [jetson, inference, memory]
  languages: [python]
  data-classification: public
---

# 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.

## Prerequisites

- Start with a current `jetson-memory-audit/scripts/audit.sh` JSON snapshot from the target Jetson.
- Know the intended workload: `llm-server`, `vlm-server`, `embedding`, or `rag`.
- If the user gives a desired free-memory target, pass it as `--target-mb`; otherwise let the script use SKU defaults.

## Available Scripts

| Script | Purpose | Arguments |
|--------|---------|-----------|
| `scripts/recommend.py` | Reads an audit JSON and emits runtime plus launch-flag recommendations. | `--audit PATH`, `--runtime`, `--workload`, `--target-mb`, `--human`. |

If your agent runtime supports `run_script`, invoke `run_script("scripts/recommend.py", ["--audit", "/tmp/audit.json", "--runtime", "auto", "--workload", "llm-server"])` and summarize the returned JSON. Otherwise run it with `python3` from the repository root.

## Instructions

1. Run `jetson-memory-audit/scripts/audit.sh` to capture the device baseline.
2. Run `scripts/recommend.py --audit /tmp/audit.json --runtime auto --workload llm-server --target-mb 6000` to get a JSON of runtime + flag recommendations.
```
END THIRD-PARTY FILE 31a923e1c08ce02f

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