Saving RAM Memory on NVIDIA Jetson with Agent Skills

Deploying AI at the edge often means fitting demanding models, multimedia pipelines, and system services into a limited memory budget. On Jetson, that budget can be constrained not only by the application itself, but also by desktop software, background services, and hardware features that a deployed product may not need.

This blog explores how NVIDIA agent skills can make memory optimization more approachable. The skills help developers inspect a device, identify likely sources of memory use, apply focused optimizations, and compare the results across tests.

A practical workflow

The workflow begins with a baseline: inspect process memory, monitor live system usage, and understand what is consuming DRAM before changing anything.

  From there, optimization can happen at two levels:

• On the running device, headless mode can disable an unused desktop environment and related background services.

• In the BSP, unused display-related memory reservations can be removed before the image is built and flashed.

It is always important to validate these results. Every change should be checked against the real workload, not only while the device is idle.

The workflow uses two complementary sets of agent skills. On the host system, BSP skills help prepare the Jetson workspace, apply hardware-aware optimizations, build and promote an image, and guide the flashing process. On the Jetson itself, device skills inspect the running system, audit memory use, apply runtime changes such as headless mode, and run benchmarks.

Together, these skills connect image-level customization with on-device measurement. The host-side workflow can reclaim memory reserved for hardware the product does not use, like display or camera, while the target-side workflow can reduce memory consumed by active services and validate the result under load.

Results from an 8 GB Jetson Orin Nano Developer Kit

In this example, disabling the desktop increased idle available memory by 757 MiB.

Under an LLM-only benchmark, the target-side headless configuration reduced:

•   RAM in use from 6235 MB to 5995 MB

•   Swap use from 610 MB to 1 MB

A combined BSP and target-side headless configuration also increased the memory visible to Linux from 7546 MB to 7614 MB. This is a different kind of improvement: it increases the system’s available capacity, while disabling running services reduces active memory consumption.

The following table summaries the main results obtained in this test:

Measurement	Base configuration	Target-side headless	BSP + target-side headless
Idle available RAM	5.82 GiB	6.56 GiB	6.66GiB
LLM-test RAM in use	6235 MB	5995 MB	5982 MB
LLM-test swap in use	610 MB	1 MB	1 MB
RAM visible to Linux	7546 MB	7546 MB	7614 MB

Table 1. Summary of memory results.

These numbers should not be treated as universal. They depend on the Jetson model, JetPack release, power mode, model, and workload. Still, they demonstrate that meaningful headroom can often be recovered without changing the application itself.

Why agent skills help

The main value of the agent skills is that they make the workflow easier to explore and repeat:

•   Setting up and customizing a BSP

•   Identifying active services and memory consumers

•   Applying reversible changes

•   Preserving context across reboots

•   Comparing before-and-after measurements

This lowers the friction of testing ideas while keeping the developer in control of the final configuration.

Takeaway

Memory optimization on Jetson is most effective when it combines measurement, targeted changes, and workload validation. Removing unused desktop functionality can reduce runtime memory pressure, while BSP customization can reclaim memory reserved for hardware features the product does not use.

From optimization experiment to product deployment

Agent skills can make it faster to explore Jetson memory optimizations, but production deployment still requires platform-specific engineering. The final configuration must preserve the services, peripherals, multimedia pipelines, and application behavior required by the product.

RidgeRun can help teams move from evaluation to deployment with Jetson BSP customization, image build and flash workflows, memory and performance profiling, multimedia and AI pipeline optimization, camera integration, and application-level validation.

Interested in optimizing your Jetson-based product for tighter memory budgets or larger edge-AI workloads? Contact RidgeRun.

Read the full guide

The full guide (Optimizing_Memory_Usage_on_NVIDIA_Jetson_with_Agent_Skills) includes setup steps, agent skills, benchmark commands, measurements, and considerations.