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deepseek-quick-deploy
Quickly deploy DeepSeek-R1 on Jetson using Ollama. Two-command setup. Requires Jetson with >8GB RAM and JetPack 5.1.1+.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Quickly deploy DeepSeek-R1 on Jetson using Ollama. Two-command setup. Requires Jetson with >8GB RAM and JetPack 5.1.1+.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Install NVIDIA-optimized PyTorch with CUDA support on reComputer Jetson devices, covering JetPack 5.x and JetPack 6.x workflows, cuSPARSELt setup, torch/torchvision pairing, probing multiple Python or conda targets, and managing multiple PyTorch versions with Miniconda.
Deploy a local AI NVR (Network Video Recorder) on Jetson Orin using NVIDIA VST and Jetson Platform Services with DeepStream pedestrian detection. Requires Jetson Orin with JetPack 6.0 and an IP camera.
Perform over-the-air JetPack/L4T updates on Jetson devices using Allxon DMS Portal. Covers OTA payload generation, upload, and fleet distribution. Requires Ubuntu host PC and Allxon DMS agent.
Install and configure Allxon DMS agent on NVIDIA Jetson devices for remote monitoring and management via the Allxon cloud portal. Supports JetPack 4.6+.
Set up the alwaysAI computer vision platform to deploy ML-based object detection on Jetson devices. Supports live camera and video file inference with TensorRT acceleration. Requires JetPack 4.6 and a host PC.
Backup and restore a reComputer Jetson system image using the L4T backup/restore script. Requires Ubuntu host, USB-C cable, and the matching JetPack BSP package.
| name | deepseek-quick-deploy |
| description | Quickly deploy DeepSeek-R1 on Jetson using Ollama. Two-command setup. Requires Jetson with >8GB RAM and JetPack 5.1.1+. |
Hardware required: Jetson with >8GB RAM, JetPack 5.1.1+. This is the fastest path to running DeepSeek-R1 locally. For optimized MLC-based deployment with better throughput, see the
deploy-deepseek-mlcskill instead.
Run one phase at a time. After each phase:
[STOP] → stop immediately, consult the failure decision tree below.[OK] → tell the user "Phase N complete" and proceed to the next phase.| Requirement | Detail |
|---|---|
| RAM | >8 GB (unified memory) |
| JetPack | 5.1.1 or later |
| Internet | Required for install script and model download |
| Disk space | ≥10 GB free for model weights |
curl -fsSL https://ollama.com/install.sh | sh
Verify:
ollama --version
# Expected: ollama version x.x.x
[OK] when ollama --version prints a version string. [STOP] if the install script fails — see failure decision tree.
ollama run deepseek-r1
The first run downloads the model weights (~4–8 GB depending on quantization). Subsequent runs start immediately from cache.
Expected: after download, an interactive prompt appears:
>>> Send a message (/? for help)
Test with a simple prompt to confirm the model responds. Type /bye to exit.
[OK] when the model responds to a prompt. [STOP] if the process is killed or hangs — see failure decision tree.
| Symptom | Action |
|---|---|
curl install script fails with network error | Check internet connectivity. If behind a proxy, set https_proxy env var before running curl. Try downloading the script manually and inspecting it. |
curl install script fails with permission error | Run with sudo or ensure /usr/local/bin is writable. |
| Model download stalls or fails mid-way | Retry ollama run deepseek-r1 — Ollama resumes partial downloads. Check available disk space: df -h. |
| Process killed during model pull (OOM) | Not enough RAM. Free memory by stopping other processes, or use a smaller quantization: ollama run deepseek-r1:1.5b. |
| Model loads but inference is very slow | Expected on smaller Jetson modules. For better performance use the deploy-deepseek-mlc skill which uses MLC-optimized kernels. |
ollama: command not found after install | Add Ollama to PATH: export PATH=$PATH:/usr/local/bin, then reload shell or open a new terminal. |
references/source.body.md — full source tutorial with additional context (reference only)