Jetson LLM Serve

NVIDIAOfficialCreated by Jetson TeamDistributed by NVIDIA

Skill
Workflow
Cloud and DevOps
Apache-2.0
Medium risk

From the author

Stand up vLLM or SGLang serving on Jetson, using upstream vLLM on Thor and Orin JetPack 7.2+, and NVIDIA-AI-IOT vLLM on older Orin.

— the description by Jetson Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a standardized procedure for deploying LLM and VLM inference servers on NVIDIA Jetson devices. It guides users through selecting the correct container runtime based on their specific Jetson generation (Thor or Orin) and JetPack version, setting optimal power modes, and launching OpenAI-compatible API servers using vLLM or SGLang. It includes pre-flight checks to ensure hardware compatibility and provides configuration templates for memory and performance tuning.

What it does

Identifies the appropriate container runtime for Jetson Thor and Orin architectures.
Provides docker run commands for vLLM and SGLang servers.
Configures system power modes for optimal inference performance.
Offers guidance on quantization formats like W4A16 and NVFP4 for Jetson hardware.
Includes pre-flight verification steps for GPU availability and environment readiness.
Supports both LLM and VLM deployment workflows.

When to use it

When you need to host an LLM or VLM on a Jetson device for local inference.
When you require an OpenAI-compatible API endpoint for your Jetson-based application.
When you need to switch between vLLM and SGLang based on specific RAG or tool-use requirements.
After determining memory constraints using memory tuning tools.

Advantages

Ensures compatibility by matching containers to specific JetPack and hardware versions.
Standardizes deployment patterns for consistent performance across Jetson product lines.
Provides clear paths for both high-throughput serving and structured generation workflows.
Includes essential pre-flight checks to prevent common deployment failures.

Usage examples

“How do I start a vLLM server on my AGX Orin running JetPack 7.2?”: The agent identifies the Orin hardware, recommends the upstream vLLM 0.20+ container, provides the specific docker run command with appropriate memory utilization flags, and outlines the verification steps.
“I need to run an SGLang server on my Jetson Thor for a RAG application.”: The agent provides the command to launch the NVIDIA SGLang 26.01 container, including the necessary environment variables and memory fraction settings for Thor.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/jetson-llm-serve/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip jetson-llm-serve.zip -d .agents/skills

Documentation by Jetson Team

Jetson LLM Serve

Encodes the Jetson AI Lab GenAI tutorial: on Orin JetPack 7.2 / L4T r39+, use upstream vLLM 0.20+ (vllm/vllm-openai:latest); on older Orin, pick the NVIDIA-AI-IOT prebuilt vLLM container; on Thor, use upstream vLLM 0.20+ or validated native vLLM 0.20+, and use NVIDIA SGLang 26.01 (nvcr.io/nvidia/sglang:26.01-py3, SGLang 0.5.5.post2) when SGLang is requested. Set MAXN, make Hugging Face credentials/cache available, and launch an OpenAI-compatible server. Works for both LLMs and VLMs.

Purpose

Provide a Jetson-appropriate serving recipe for an LLM or VLM using vLLM or SGLang, including runtime path, launch command, endpoint, and verification step.

When to use

"Run / serve / host this model on a Jetson."
"Start a vLLM server I can hit from Open WebUI / my app."
After jetson-inference-mem-tune produced launch flags and the user wants to actually start the server.

For recipe-only questions, answer from this document without starting containers. Run live pre-flight checks only when the user asks you to check this device or execute the deployment.

Prerequisites

Run on the Jetson host or a shell with Docker access to the Jetson GPU runtime.
Know the target Jetson generation (thor or orin) and the model identifier or local checkpoint path.
Use HF_TOKEN only when the model is gated/private; public models should omit the token environment variable.
Use jetson-inference-mem-tune first when memory headroom or launch flags are uncertain.

Instructions

For recipe questions, provide a complete launch recipe instead of trying to call jetson-llm-serve as a tool. A complete answer includes:

The Jetson-appropriate runtime path: upstream vLLM 0.20+ (vllm/vllm-openai:latest) or NVIDIA SGLang 26.01 (nvcr.io/nvidia/sglang:26.01-py3, SGLang 0.5.5.post2) on Thor, NVIDIA-AI-IOT vLLM container on older Orin, or upstream vLLM 0.20+ on Orin JetPack 7.2 / L4T r39+.
The model checkpoint / Hugging Face repo the user named.
A docker run + server command sketch with --host 0.0.0.0 --port 8000.
The OpenAI-compatible endpoint: http://<jetson-ip>:8000/v1.
A verification step such as curl http://localhost:8000/v1/models.

For VLM questions, explicitly say the VLM uses the same vLLM serving flow as an LLM with a different vision-language checkpoint. Do not omit vLLM or the Jetson container when answering VLM prompts.

Step 1 — Pick the runtime path (per Jetson family)

Use upstream vLLM 0.20+ on Thor (vllm/vllm-openai:latest, or a validated native vLLM 0.20+ install). On Orin JetPack 7.2 / L4T r39+, use upstream vLLM 0.20+ (vllm/vllm-openai:latest). On older Orin releases, use the NVIDIA-AI-IOT prebuilt vLLM image (packages) because it ships the correct CUDA / cuDNN / TensorRT stack for that JetPack. Use NVIDIA SGLang 26.01 (nvcr.io/nvidia/sglang:26.01-py3, SGLang 0.5.5.post2) on Thor when the user asks for SGLang, RAG, tool-use, or programmable serving; do not recommend native upstream SGLang on Orin unless a JetPack-matched release explicitly supports it.

Jetson family	Runtime path
Thor (T5000, T4000)	upstream vLLM 0.20+ (`vllm/vllm-openai:latest`) or NVIDIA SGLang 26.01 (`nvcr.io/nvidia/sglang:26.01-py3`, SGLang 0.5.5.post2)
AGX Orin / Orin NX / Nano	Orin JetPack 7.2 / L4T r39+: upstream vLLM 0.20+ (`vllm/vllm-openai:latest`); older Orin: `ghcr.io/nvidia-ai-iot/vllm:latest-jetson-orin`

To detect the silicon era for image tags:

Source the detector so exports survive in your shell:
```
. skills/jetson-diagnostic/scripts/detect_jetson.sh
```
Check JETSON_GENERATION (thor or orin) and choose the matching runtime path from the table above.
Use JETSON_PRODUCT_LINE for a finer bucket such as thor-agx or orin-nano; JETSON_SKU remains the legacy identifier.

Do not use bash skills/jetson-diagnostic/scripts/detect_jetson.sh when you need exported variables in the caller; running with bash uses a subshell.

Step 2 — Set MAXN power mode

sudo nvpmodel -m 0 && sudo jetson_clocks

Skip this only if the user explicitly asks for a power-constrained run; otherwise benchmark and serving numbers will be inconsistent.

Step 3 — Run the server

On Thor with vLLM, use upstream vLLM 0.20+ (vllm/vllm-openai:latest) or a validated native vLLM 0.20+ install:

docker run --rm -it --runtime nvidia --network host --ipc host --name vllm \
  -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \
  -e HF_TOKEN="$HF_TOKEN" \
  vllm/vllm-openai:latest \
  vllm serve <hf-repo-id> \
    --host 0.0.0.0 --port 8000 \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.75 \
    --tensor-parallel-size 1

On Orin JetPack 7.2 / L4T r39+, use upstream vLLM 0.20+ (vllm/vllm-openai:latest). On older Orin releases, use the NVIDIA-AI-IOT container:

docker run --rm -it --runtime nvidia --network host --name vllm \
  -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \
  -e HF_TOKEN="$HF_TOKEN" \
  ghcr.io/nvidia-ai-iot/vllm:latest-jetson-orin \
  vllm serve <hf-repo-id> \
    --host 0.0.0.0 --port 8000 \
    --max-model-len 4096 \
    --gpu-memory-utilization 0.85 \
    --tensor-parallel-size 1

HF_TOKEN is required only for gated/private Hugging Face models; omit the -e HF_TOKEN="$HF_TOKEN" line for public models that do not need Hub authentication. Passing HF_TOKEN as an environment variable can expose it through Docker inspect output, process metadata, or logs on shared systems. Prefer the narrowest-scoped token possible, rotate/revoke it after shared-container use, and use a mounted credential file or Docker secret when the deployment environment supports that pattern.

Wait for Application startup complete. Server is on http://0.0.0.0:8000/v1.

For SGLang on Thor, use NVIDIA SGLang 26.01 (nvcr.io/nvidia/sglang:26.01-py3), which packages SGLang 0.5.5.post2 and lists Jetson Thor support. Do not judge Thor SGLang support from older prerelease SGLang results:

docker run --rm -it --runtime nvidia --network host --ipc host --name sglang \
  -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \
  -e HF_TOKEN="$HF_TOKEN" \
  nvcr.io/nvidia/sglang:26.01-py3 \
  python3 -m sglang.launch_server \
    --model-path <hf-repo-id> \
    --host 0.0.0.0 \
    --port 8000 \
    --mem-fraction-static 0.60 \
    --max-running-requests 8

Use SGLang when the user needs RAG/tool-use workflows, structured generation, or SGLang-specific scheduling. For plain high-throughput OpenAI-compatible serving, prefer vLLM unless the user asks for SGLang.

SKU-appropriate defaults

Knob	Orin Nano / NX	AGX Orin / Thor
`--max-model-len`	`4096`	`8192`
`--gpu-memory-utilization`	`0.85`	`0.85`
`--tensor-parallel-size`	`1`	`1`

If the server OOMs at startup, lower --gpu-memory-utilization by 0.05 and re-launch (or run jetson-inference-mem-tune for a workload-aware recommendation).

Quantization preferences (matters more than the runtime)

For vLLM and SGLang, choose checkpoint formats by Jetson family:

Jetson family	First choice	Acceptable fallback
Thor	NVFP4 when the model/runtime supports it	W4A16
Orin Nano / NX	W4A16	AWQ or GPTQ 4-bit
AGX Orin	W4A16	AWQ or GPTQ 4-bit

For llama.cpp and Ollama, use GGUF model quantization names instead: recommend INT4 / Q4_K_M GGUF on both Orin and Thor, and choose a smaller INT4 GGUF model if memory is tight. Do not call GGUF Q4_K_M a W4A16/AWQ/GPTQ model. NVFP4 is Thor-preferred and Thor-tuned for runtimes that support it.

VLM mode

VLMs use the same flow as LLMs: same container, same vllm serve invocation, different vision-language checkpoint. The container handles image preprocessing. For a VLM-specific browser UI, use the live-vlm-webui container; for a generic chat UI for either, use Open WebUI pointed at http://<jetson-ip>:8000/v1.

Do not fabricate device capacity

Do not invent RAM totals, free-memory values, model sizes, JetPack versions, or SKU/variant names when giving a serving recipe. If capacity matters, either run the live pre-flight checks (when execution is allowed) or hand off to jetson-inference-mem-tune / jetson-memory-audit. If live data is not available, say the value is unknown and provide conservative defaults instead of quoting a made-up number.

Pre-flight checklist (the agent should verify before running Step 3)

[ ] On a Jetson (/proc/device-tree/model contains NVIDIA Jetson).
[ ] nvpmodel -q reports a recognized max-performance mode: MAXN or MAXN_* such as MAXN_SUPER. Wattage-named modes should be reported as warnings unless the user explicitly confirms they are the intended benchmark mode for that device.
[ ] On Thor, check whether MIG is enabled before launching (nvidia-smi -L and nvidia-smi mig -lgi). If MIG is enabled, warn that vLLM/SGLang may see only a MIG slice or no CUDA device.
[ ] On Thor with MIG or display/camera contention, inspect GPU users with sudo lsof /dev/nvidia*. Display managers, Xorg/GNOME, or nvargus-daemon may hold GPU device files; do not stop services or change MIG mode unless the user explicitly approves.
[ ] No container named vllm already running (docker ps --format '{{.Names}}'); otherwise docker rm -f vllm first.
[ ] Docker exposes the NVIDIA runtime (docker info | grep -i 'runtimes.*nvidia'), or a GPU-enabled container can run nvidia-smi.
[ ] ~/.cache/huggingface exists; HF_TOKEN is set if the model is gated.

Limitations

This skill provides serving commands and pre-flight checks; it does not benchmark the deployed server.
Container tags such as latest are mutable. For release or compliance deployments, pin a digest and record it with the deployment notes.
vLLM and SGLang memory limits still depend on model architecture, quantization, context length, and concurrent request count. Use jetson-inference-mem-tune when a command OOMs or memory headroom matters.
Thor vLLM requires upstream vLLM 0.20+ or newer. Older upstream vLLM images may not support Thor / SM 11.0 correctly.
Thor SGLang should use NVIDIA SGLang 26.01 or newer release notes that explicitly list Jetson Thor support. NVIDIA SGLang 26.01 contains SGLang 0.5.5.post2.
On Thor, MIG, desktop display, or camera services can hide the full GPU from containers. This skill should detect and warn only; disabling MIG or stopping services such as gdm3 or nvargus-daemon requires explicit user approval.
Host-native vLLM/SGLang on Thor should be used only when that install is already validated on the target JetPack.

Hand off to

jetson-llm-benchmark to actually measure the deployed server.
jetson-speculative-decoding to add EAGLE-3 / draft-model speculation by appending --speculative-config '{...}' to the vllm serve command above.
jetson-inference-mem-tune if the server OOMs or is memory-bound.

Source

Jetson AI Lab — Introduction to GenAI on Jetson: How to Run LLMs and VLMs and NVIDIA-AI-IOT GHCR packages.

Files

ATTRIBUTION.mdAdded by AutoGineer911 B
BENCHMARK.md3.2 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md11.8 KiB
evals/evals.json5.7 KiB
skill-card.md3.6 KiB
skill.oms.sig4.5 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs network access, Needs a command-line tool, Needs Python

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs
Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by

Jetson Team

Publisher

NVIDIA (Official)

Distributed by

NVIDIA

Source

NVIDIA/skills · skills/jetson-llm-serve · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by Jetson Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download jetson-llm-serve.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Jetson Team

Jetson LLM Serve

Purpose

When to use

Prerequisites

Instructions

Step 1 — Pick the runtime path (per Jetson family)

Step 2 — Set MAXN power mode

Step 3 — Run the server

SKU-appropriate defaults

Quantization preferences (matters more than the runtime)

VLM mode

Do not fabricate device capacity

Pre-flight checklist (the agent should verify before running Step 3)

Limitations

Hand off to

Source

Files

LICENSE

Requirements and compatibility

Security

Attribution and licence

Related skills

Shipping And Launch

Quantized Export

Deployment Pipeline Design

Gitops Workflow

Spark Environment Setup

Cloud Infrastructure Deployment Engineer

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