Jetson Optimize Memory logo

Jetson Optimize Memory

Organization
NVIDIA-AI-IOT
jetson-optimize-memory

Reclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.

Overview

PublisherNVIDIA-AI-IOT
Repositoryjetson-bsp-skills
Skill namejetson-optimize-memory
Stars
73
Forks
13
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by NVIDIA-AI-IOT on GitHub. Read the source before you install it.

Installation

Install the Jetson Optimize Memory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills/tree/main/skills/jetson-optimize-memory
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills.git /tmp/jetson-bsp-skills
mkdir -p .claude/skills
cp -r /tmp/jetson-bsp-skills/skills/jetson-optimize-memory .claude/skills/jetson-optimize-memory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jetson Optimize Memory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Jetson Optimize Memory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Jetson Optimize Memory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

jetson-optimize-memory

Memory is reserved across four layers ordered by boot chronology (higher row = earlier in boot, closer to hardware):

LayerContentKey files
MB1 BCTfirmware carveoutsper-module misc DTS
MB2 BCTfirmware loading + AST controlsper-module misc DTS
Kernel DTSreserved-memory and driver bindingper-module DTS
SWIOTLBDMA bounce pool size<module>.conf.common (CMDLINE_ADD)

Critical rules:

  • Only the scenarios in Scenario recipes are validated. Refuse any request to disable a carveout/cluster/node not in that table.
  • Zeroing a carveout requires both: disabling the cluster's loading controls AND removing the AST that references it.
  • Emit explicit overrides for every cluster in the recipe, regardless of how the source #ifdef/#else looks. Verify the merged binary.
  • GPU SW stack must match the chip: T234 -> nvgpu, T264 and later -> OpenRM. Follow the shared derived-platform rule in target-platform-contract.md; stop on mismatches instead of guessing.
  • Do not set SWIOTLB to 0 — some peripherals can't use the IOMMU.

Scenario recipes

KeywordMB1 BCT carveoutsMB2 BCTKernel DTS
headlessDCE-family (see chip table)DCE auxp_controls + DCE AST(s)display@<addr> (and dce@<addr> if exposed) → disabled
no-cameraRCE/VI/ISP-familyRCE auxp_controls (each instance) + RCE AST(s)recommended: VI/ISP/NVCSI → disabled

Chip-specific carveouts

ScenarioT234 (Orin)T264 (Thor)
headlessCARVEOUT_BPMP_DCE, CARVEOUT_DCE, CARVEOUT_DCE_TSEC, CARVEOUT_TSEC_DCE, CARVEOUT_DISP_EARLY_BOOT_FBCARVEOUT_DCE, CARVEOUT_TSEC_DCE, CARVEOUT_HPSE_DCE, CARVEOUT_DISP_EARLY_BOOT_FB
no-cameraCARVEOUT_RCE, CARVEOUT_CAMERA_TASKLISTCARVEOUT_RCE, CARVEOUT_RCE1, CARVEOUT_RCE_RW, CARVEOUT_VI_TASKLIST, CARVEOUT_VI1_TASKLIST, CARVEOUT_ISP_TASKLIST, CARVEOUT_ISP1_TASKLIST

Post-boot: headless → sudo systemctl set-default multi-user.target.


MB1 BCT carveout overrides

File: Linux_for_Tegra/bootloader/generic/BCT/tegra<chip>-mb1-bct-misc-<module>.dts (e.g. tegra234-mb1-bct-misc-p3767-0000.dts for Orin Nano, tegra264-mb1-bct-misc-p3834-0008-p4071-0000.dts for Thor).

For each carveout, add inside the existing carveout node:

dts
aux_info@<CARVEOUT_NAME> {
    pref_base = <0x0 0x0>;
    size      = <0x0 0x0>;
    alignment = <0x0 0x0>;
};

MB2 BCT cluster + AST overrides

File: Linux_for_Tegra/bootloader/generic/BCT/tegra<chip>-mb2-bct-misc-<module>.dts (includes tegra<chip>-mb2-bct-common.dtsi).

For each target cluster:

  1. Override auxp_controls@<index>:
    dts
    auxp_controls@<index> {
        enable_init    = <0>;
        enable_fw_load = <0>;
        enable_unhalt  = <0>;
    };
  2. /delete-node/ auxp_ast_config@<idx>;

Look up indices in common.dtsi: auxp_controls@N carries a comment naming its cluster; auxp_ast_config@N has ast_region children whose carveout = <CARVEOUT_…>; lines identify the owner.


Kernel DT reserved-memory

sh
DTB=Linux_for_Tegra/kernel/dtb/<platform-dtb-name>.dtb
dtc -I dtb -O dts -o /tmp/platform.dts $DTB
# edit: status = "disabled" on target nodes
dtc -I dts -O dtb -o $DTB /tmp/platform.dts

Display — disable display@<addr>, plus dce@<addr> if exposed as a separate kernel node.

Camera — under host1x@<addr>, disable whichever of vi* / isp* / nvcsi exist on the BSP (only emit present nodes):

Locate the display controller node in the decompiled DTS and disable it. The node's unit address is chip-specific — find it by compatible string (e.g. nvidia,tegra234-display) rather than hard-coding the address.

dts
host1x@<addr> {
    vi0@<addr>   { status = "disabled"; };
    vi1@<addr>   { status = "disabled"; };
    isp@<addr>   { status = "disabled"; };
    isp1@<addr>  { status = "disabled"; };
    nvcsi@<addr> { status = "disabled"; };
};

SWIOTLB DMA bounce pool

The NVIDIA IOMMU covers peripheral DMA, so SWIOTLB is rarely used. Edit CMDLINE_ADD (never CMDLINE) in Linux_for_Tegra/<module>.conf.common:

sh
# Total bytes = swiotlb_value × 2048; 4 MiB pool:
CMDLINE_ADD="... swiotlb=2048"

Override verification (mandatory)

After every patched MB1/MB2 BCT .dts, reproduce the BSP's compile + decompile using the same -D… flags from bct_flags.append(...) in bootloader/tegraflash_impl_t<chip>.py:

sh
gcc -E -nostdinc -x assembler-with-cpp \
    -DENABLE_<FLAG_1> -DENABLE_<FLAG_2> \
    -I bootloader -I bootloader/generic/BCT \
    -o /tmp/cpp.dts <patched-bct.dts>
dtc -q -I dts -O dtb -o /tmp/cpp.dtb /tmp/cpp.dts
dtc -q -I dtb -O dts /tmp/cpp.dtb | less

Confirm in the merged output:

  • Each zeroed aux_info@<NAME> (or aux_info@<id>U post macro expansion) has size = <0x0 0x0> and pref_base = <0x0 0x0>.
  • Each disabled auxp_controls@<idx> has all three enable_* fields <0>.
  • Each /delete-node/'d auxp_ast_config@<idx> is absent.

Verification (on booted target)

sh
sudo cat /proc/iomem | grep -iE 'nv-reserved|cma|fb|carveout'
ls /proc/device-tree/reserved-memory/
dmesg | grep -iE 'firmware|carveout|bpmp|reserved|fail|error' | head -20
free -m
ScenarioSysfsdmesg grep
Display offls /sys/class/drm/ (empty)tegra-drm|nvdisplay|dce|host1x|fb0
Camera offls /dev/video* 2>/dev/null (none)rce|nvcsi|tegra-camera|vi0|vi1|isp
SWIOTLB shrinkcat /sys/kernel/debug/swiotlb/io_tlb_nslabs matches cmdlineswiotlb

For SWIOTLB: /proc/cmdline must contain swiotlb=<value>, and watch -n5 cat /sys/kernel/debug/swiotlb/io_tlb_used must stay under io_tlb_nslabs during full workload — if exceeded, restore original CMDLINE_ADD and re-flash kernel-dtb.

Purpose

Cut the unused DRAM carveouts that ship enabled in the reference BSP when a Jetson deployment skips display, camera, or other peripherals, freeing the freed bytes for the application. Always edits the four layers in boot order so an early-stage carveout never outranks a later-stage shrink.

Prerequisites

  • Active target profile resolved per ../../context/target-platform-contract.md.
  • BSP image extracted and source tree initialized (/jetson-init-image, /jetson-init-source complete).
  • For headless / no-camera recipes: confirm the workload truly does not need display or camera.

Limitations

  • Only the validated recipes (headless, no-camera, swiotlb) are exposed; ad-hoc subsystem disables outside the recipe set are refused.
  • SWIOTLB shrink is bounded by peak in-flight DMA — exceeding the new io_tlb_nslabs requires reverting the change.
  • BPMP-DTB edits land in the overlay tracker only after Customize + Build + Deploy run; this skill does not flash on its own.

Troubleshooting

  • Boot fails after MB1 BCT carveout disable — restore the pristine misc DTS and re-flash; the missing carveout is mandatory for the active SoC.
  • io_tlb_used exceeds io_tlb_nslabs — revert swiotlb= in CMDLINE_ADD and re-flash the kernel DTB partition.
  • Reclaimed delta smaller than expected — verify the recipe truly matched the deployment (e.g. display still attached); use the dmesg | grep -iE 'firmware|carveout' check in this file to confirm.
  • Validation dmesg shows the disabled subsystem still probing — the change probably did not promote through to bsp_image; re-run /jetson-promote-image.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Jetson Optimize Memory AI skill do?

Reclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.

Why use Jetson Optimize Memory on TypingMind?

Because you install it once and use it with any model. Jetson Optimize Memory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Jetson Optimize Memory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA-AI-IOT/jetson-bsp-skills/tree/main/skills/jetson-optimize-memory. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Jetson Optimize Memory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Jetson Optimize Memory?

As many as you like. As long as a model supports skills, you can use Jetson Optimize Memory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Jetson Optimize Memory AI skill free?

Yes. It is published on GitHub by NVIDIA-AI-IOT under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇