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| ╭─── Claude Code v2.0.14 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ | |
| │ │ Tips for getting started │ | |
| │ Welcome back Simon! │ Run /init to create a CLAUDE.md file with instructions for Claude │ | |
| │ │ Note: You have launched claude in your home directory. For the best experience, launch it in a project… │ | |
| │ ▐▛███▜▌ │ ─────────────────────────────────────────────────────────────────────────────────────────────────────── │ | |
| │ ▝▜█████▛▘ │ Recent activity │ | |
| │ ▘▘ ▝▝ │ No recent activity │ | |
| │ │ │ | |
| │ Sonnet 4.5 · Claude Max │ │ | |
| │ /home/dev │ │ | |
| ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ | |
| > Tell me things about this environment, in particular the CUDA abilities | |
| ● I'll gather information about your environment and CUDA capabilities. | |
| ● Bash(nvidia-smi) | |
| ⎿ Mon Oct 13 20:54:05 2025 | |
| +-----------------------------------------------------------------------------------------+ | |
| | NVIDIA-SMI 580.82.09 Driver Version: 580.82.09 CUDA Version: 13.0 | | |
| … +17 lines (ctrl+o to expand) | |
| ● Bash(nvcc --version) | |
| ⎿ Error: /bin/bash: line 1: nvcc: command not found | |
| ● Bash(cat /usr/local/cuda/version.txt 2>/dev/null || cat /usr/local/cuda/version.json 2>/dev/null || echo "CUDA version file not found") | |
| ⎿ { | |
| "cuda" : { | |
| "name" : "CUDA SDK", | |
| … +151 lines (ctrl+o to expand) | |
| ● Bash(lscpu | grep -E "Model name|CPU\(s\)|Thread|Core") | |
| ⎿ CPU(s): 20 | |
| On-line CPU(s) list: 0-19 | |
| Model name: Cortex-X925 | |
| … +8 lines (ctrl+o to expand) | |
| ● Bash(free -h) | |
| ⎿ total used free shared buff/cache available | |
| Mem: 119Gi 7.5Gi 100Gi 17Mi 12Gi 112Gi | |
| Swap: 0B 0B 0B | |
| ● Bash(python3 --version 2>&1 && python --version 2>&1) | |
| ⎿ Error: Python 3.12.3 | |
| /bin/bash: line 1: python: command not found | |
| ● Bash(ls -la /usr/local/cuda* 2>/dev/null | head -20) | |
| ⎿ lrwxrwxrwx 1 root root 22 Sep 8 10:33 /usr/local/cuda -> /etc/alternatives/cuda | |
| lrwxrwxrwx 1 root root 25 Sep 8 10:33 /usr/local/cuda-13 -> /etc/alternatives/cuda-13 | |
| … +17 lines (ctrl+o to expand) | |
| ● Bash(python3 -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA Available: {torch.cuda.is_available()}'); | |
| print(f'CUDA Version: {torch.version.cu…) | |
| ⎿ Error: Traceback (most recent call last): | |
| File "<string>", line 1, in <module> | |
| ModuleNotFoundError: No module named 'torch' | |
| ● Bash(df -h /home/dev) | |
| ⎿ Filesystem Size Used Avail Use% Mounted on | |
| overlay 3.7T 194G 3.3T 6% / | |
| ● Bash(python3 -c "import sys; import pkg_resources; packages = [p.project_name for p in pkg_resources.working_set]; ml_packages = [p for | |
| p in packages if any(x in p.l…) | |
| ⎿ <string>:1: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.ht | |
| ml | |
| No common ML packages found | |
| ● Bash(echo $PATH | tr ':' '\n' | grep -i cuda) | |
| ⎿ No matches found | |
| ● Bash(echo $LD_LIBRARY_PATH | tr ':' '\n' | grep -i cuda) | |
| ⎿ No matches found | |
| ● Bash(uname -m && arch) | |
| ⎿ aarch64 | |
| aarch64 | |
| ● Environment Summary | |
| System Overview | |
| - OS: Linux 6.11.0-1014-nvidia (Ubuntu/Debian-based) | |
| - Architecture: aarch64 (ARM64) | |
| - CPU: ARM-based with 20 cores | |
| - 10x Cortex-X925 cores (performance cores) | |
| - 10x Cortex-A725 cores (efficiency cores) | |
| - RAM: 119 GB total (112 GB available) | |
| - Storage: 3.7 TB total, 3.3 TB available (6% used) | |
| - Python: 3.12.3 | |
| CUDA Capabilities | |
| GPU Hardware: | |
| - GPU: NVIDIA GB10 (Blackwell architecture - latest generation) | |
| - Temperature: 33°C (idle) | |
| - Power: 4W (idle state) | |
| - Status: Persistence mode enabled, no running processes | |
| CUDA Software: | |
| - Driver Version: 580.82.09 | |
| - CUDA SDK: 13.0.1 | |
| - Installation: /usr/local/cuda-13.0 | |
| - Included Tools: | |
| - NVCC compiler (13.0.88) - installed but not in PATH | |
| - cuBLAS 13.0.2.14 (linear algebra) | |
| - cuFFT 12.0.0.61 (fast Fourier transforms) | |
| - cuRAND 10.4.0.35 (random number generation) | |
| - cuSOLVER 12.0.4.66 (linear solvers) | |
| - cuSPARSE 12.6.3.3 (sparse matrix operations) | |
| - NVML, NVRTC, CUPTI (profiling and runtime compilation) | |
| - Nsight Compute 2025.3.1.4 (profiling) | |
| - Nsight Systems 2025.3.2.474 (system profiling) | |
| - GPUDirect Storage 1.15.1.6 | |
| Current Limitations: | |
| - CUDA tools not in PATH (nvcc not accessible by default) | |
| - No ML frameworks installed (PyTorch, TensorFlow, JAX, etc.) | |
| - No scientific computing libraries (NumPy, SciPy, Pandas, CuPy) | |
| - LD_LIBRARY_PATH not configured for CUDA | |
| This is a high-performance ARM-based system with cutting-edge NVIDIA GPU support and CUDA 13.0, ideal for GPU-accelerated | |
| computing, ML/AI workloads, or CUDA development once the appropriate frameworks are installed. | |
| > create a python virtual environment and then try to install torch and torch.cuda to say that the GPU is visible | |
| ● I'll create a Python virtual environment and install PyTorch with CUDA support, then verify the GPU is visible. | |
| ● Bash(python3 -m venv ~/pytorch_env) | |
| ⎿ Error: The virtual environment was not created successfully because ensurepip is not | |
| available. On Debian/Ubuntu systems, you need to install the python3-venv | |
| package using the following command. | |
| apt install python3.12-venv | |
| You may need to use sudo with that command. After installing the python3-venv | |
| package, recreate your virtual environment. | |
| Failing command: /home/dev/pytorch_env/bin/python3 | |
| ● Bash(sudo apt install -y python3.12-venv) timeout: 3m 0s | |
| ⎿ Reading package lists... | |
| Building dependency tree... | |
| Reading state information... | |
| … +32 lines (ctrl+o to expand) | |
| ● Bash(python3 -m venv ~/pytorch_env) | |
| ⎿ (No content) | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install --upgrade pip) timeout: 3m 0s | |
| ⎿ Requirement already satisfied: pip in ./pytorch_env/lib/python3.12/site-packages (24.0) | |
| Collecting pip | |
| Downloading pip-25.2-py3-none-any.whl.metadata (4.7 kB) | |
| … +8 lines (ctrl+o to expand) | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install torch torchvision torchaudio) timeout: 10m 0s | |
| ⎿ Collecting torch | |
| Downloading torch-2.8.0-cp312-cp312-manylinux_2_28_aarch64.whl.metadata (30 kB) | |
| Collecting torchvision | |
| … +54 lines (ctrl+o to expand) | |
| ● Bash(source ~/pytorch_env/bin/activate && python3 -c " | |
| import torch…) | |
| ⎿ ============================================================ | |
| PyTorch Installation and CUDA Information | |
| ============================================================ | |
| … +32 lines (ctrl+o to expand) | |
| ● The installed PyTorch version (2.8.0+cpu) is CPU-only and doesn't have CUDA support. Let me install the CUDA-enabled version for | |
| ARM64. | |
| ● Bash(source ~/pytorch_env/bin/activate && pip uninstall -y torch torchvision torchaudio) | |
| ⎿ Found existing installation: torch 2.8.0 | |
| Uninstalling torch-2.8.0: | |
| Successfully uninstalled torch-2.8.0 | |
| … +6 lines (ctrl+o to expand) | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install torch torchvision torchaudio --index-url timeout: 10m | |
| https://download.pytorch.org/whl/cu124) 0s | |
| ⎿ Looking in indexes: https://download.pytorch.org/whl/cu124 | |
| Collecting torch | |
| Downloading https://download.pytorch.org/whl/cu124/torch-2.5.1-cp312-cp312-linux_aarch64.whl (2359.8 MB) | |
| … +28 lines (ctrl+o to expand) | |
| ● Bash(source ~/pytorch_env/bin/activate && python3 -c " | |
| import torch…) | |
| ⎿ Error: /home/dev/pytorch_env/lib/python3.12/site-packages/torch/cuda/__init__.py:235: UserWarning: | |
| NVIDIA GB10 with CUDA capability sm_121 is not compatible with the current PyTorch installation. | |
| The current PyTorch install supports CUDA capabilities sm_50 sm_80 sm_86 sm_89 sm_90 sm_90a. | |
| If you want to use the NVIDIA GB10 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/ | |
| warnings.warn( | |
| Traceback (most recent call last): | |
| File "<string>", line 27, in <module> | |
| File "/home/dev/pytorch_env/lib/python3.12/site-packages/torch/_tensor.py", line 1053, in __format__ | |
| return object.__format__(self, format_spec) | |
| … +44 lines (ctrl+o to see all) | |
| ● Summary | |
| Good news: The GPU is visible to PyTorch! | |
| PyTorch Version: 2.5.1 | |
| CUDA Available: True | |
| CUDA Version (compiled): 12.4 | |
| cuDNN Version: 90100 | |
| Number of CUDA Devices: 1 | |
| Device 0: NVIDIA GB10 | |
| - Device Capability: (12, 1) | |
| - Total Memory: 119.68 GB | |
| - Multi-processor Count: 48 | |
| Issue: Your NVIDIA GB10 (Blackwell architecture) has compute capability sm_121, which is very new. The current PyTorch 2.5.1 build | |
| was compiled with support for older architectures (sm_50, sm_80, sm_86, sm_89, sm_90, sm_90a) but not sm_121 yet. | |
| This means: | |
| - ✓ PyTorch can detect and see the GPU | |
| - ✗ PyTorch cannot execute operations on it (no kernel image available) | |
| Solutions: | |
| 1. Try PyTorch nightly build (may have sm_121 support): | |
| source ~/pytorch_env/bin/activate | |
| pip uninstall -y torch torchvision torchaudio | |
| pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124 | |
| 2. Wait for official PyTorch release with sm_121 support | |
| 3. Build PyTorch from source with sm_121 support (more complex) | |
| The environment is otherwise perfectly set up for CUDA development - you have CUDA 13.0 installed, 119 GB of GPU memory, and the | |
| driver is working correctly. | |
| > OK you try a pytorch nightly then | |
| ● Bash(source ~/pytorch_env/bin/activate && pip uninstall -y torch torchvision torchaudio) | |
| ⎿ Found existing installation: torch 2.5.1 | |
| Uninstalling torch-2.5.1: | |
| Successfully uninstalled torch-2.5.1 | |
| … +6 lines (ctrl+o to expand) | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install --pre torch torchvision torchaudio --index-url timeout: 10m 0s | |
| https://download.pytorch.org/whl/nightly/cu124) | |
| ⎿ Error: ERROR: Could not find a version that satisfies the requirement torch (from versions: none) | |
| ERROR: No matching distribution found for torch | |
| Looking in indexes: https://download.pytorch.org/whl/nightly/cu124 | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install --pre torch torchvision torchaudio --index-url timeout: 10m 0s | |
| https://download.pytorch.org/whl/nightly/cu121) | |
| ⎿ Error: ERROR: Could not find a version that satisfies the requirement torch (from versions: none) | |
| ERROR: No matching distribution found for torch | |
| Looking in indexes: https://download.pytorch.org/whl/nightly/cu121 | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install --pre torch torchvision torchaudio --index-url timeout: 10m 0s | |
| https://download.pytorch.org/whl/nightly/cpu) | |
| ⎿ Looking in indexes: https://download.pytorch.org/whl/nightly/cpu | |
| Collecting torch | |
| Downloading https://download.pytorch.org/whl/nightly/cpu/torch-2.10.0.dev20251013%2Bcpu-cp312-cp312-manylinux_2_28_aarch64.whl | |
| … +37 lines (ctrl+o to expand) | |
| ● Bash(source ~/pytorch_env/bin/activate && pip uninstall -y torch torchvision torchaudio) | |
| ⎿ Found existing installation: torch 2.10.0.dev20251013+cpu | |
| Uninstalling torch-2.10.0.dev20251013+cpu: | |
| Successfully uninstalled torch-2.10.0.dev20251013+cpu | |
| … +6 lines (ctrl+o to expand) | |
| ● Web Search("PyTorch ARM64 CUDA sm_121 Blackwell GB10 support 2025") | |
| ⎿ Did 6 searches in 70s | |
| ● Bash(pip search nvidia-pyindex 2>&1 || echo "pip search is disabled") | |
| ⎿ /bin/bash: line 1: pip: command not found | |
| pip search is disabled | |
| ● Fetch(https://pytorch.org/blog/pytorch-2-7/) | |
| ⎿ Received 347.8KB (200 OK) | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install torch==2.7.0 torchvision torchaudio --index-url timeout: 10m | |
| https://download.pytorch.org/whl/cu124) 0s | |
| ⎿ Error: ERROR: Could not find a version that satisfies the requirement torch==2.7.0 (from versions: 2.4.0, 2.4.1, 2.5.0, 2.5.1) | |
| ERROR: No matching distribution found for torch==2.7.0 | |
| Looking in indexes: https://download.pytorch.org/whl/cu124 | |
| ● Bash(source ~/pytorch_env/bin/activate && pip index versions torch 2>&1 | head -30) | |
| ⎿ torch (2.8.0) | |
| Available versions: 2.8.0, 2.7.1, 2.7.0, 2.6.0, 2.5.1, 2.5.0, 2.4.1, 2.4.0, 2.3.1, 2.3.0, 2.2.2, 2.2.1, 2.2.0 | |
| ● Bash(source ~/pytorch_env/bin/activate && pip install torch==2.8.0 torchvision torchaudio) timeout: 10m 0s | |
| ⎿ Collecting torch==2.8.0 | |
| Using cached torch-2.8.0-cp312-cp312-manylinux_2_28_aarch64.whl.metadata (30 kB) | |
| Collecting torchvision | |
| … +22 lines (ctrl+o to expand) | |
| ● Bash(source ~/pytorch_env/bin/activate && pip uninstall -y torch torchvision torchaudio && pip install torch torchvision timeout: 10m | |
| torchaudio --index-url https://download.pyto…) 0s | |
| ⎿ Found existing installation: torch 2.8.0 | |
| Uninstalling torch-2.8.0: | |
| Successfully uninstalled torch-2.8.0 | |
| … +40 lines (ctrl+o to expand) | |
| ● Perfect! I've installed PyTorch 2.7.1 with CUDA 12.8 support and Triton 3.3.1 (which has Blackwell support according to the release | |
| notes). Let me test if this works with your GB10 GPU: | |
| ● Bash(source ~/pytorch_env/bin/activate && python3 -c " | |
| import torch…) | |
| ⎿ ============================================================ | |
| PyTorch Installation and CUDA Information | |
| ============================================================ | |
| … +38 lines (ctrl+o to expand) | |
| ● Success! 🎉 | |
| PyTorch 2.7.1 with CUDA 12.8 works perfectly with your NVIDIA GB10 GPU! | |
| Results: | |
| PyTorch Version: 2.7.1+cu128 | |
| CUDA Available: True | |
| CUDA Version (compiled): 12.8 | |
| cuDNN Version: 90800 | |
| Device 0: NVIDIA GB10 | |
| - Device Capability: (12, 1) | |
| - Total Memory: 119.68 GB | |
| - Multi-processor Count: 48 | |
| ✓ GPU is visible and working correctly! | |
| All GPU operations tested successfully: | |
| - ✓ Tensor creation and transfer to GPU | |
| - ✓ Basic arithmetic operations on GPU | |
| - ✓ Matrix multiplication on GPU | |
| What Made It Work: | |
| PyTorch 2.7.1 with CUDA 12.8 includes: | |
| - Support for Blackwell architecture (sm_121) | |
| - Triton 3.3.1 with Blackwell compatibility | |
| - Pre-built ARM64 wheels with full CUDA support | |
| Virtual Environment Location: | |
| source ~/pytorch_env/bin/activate | |
| You now have a fully functional PyTorch environment that can leverage your GB10's 119.68 GB of GPU memory and 48 multi-processors! | |
| > Write detailed notes on how we got here | |
| ● Write(pytorch_cuda_setup_notes.md) | |
| ⎿ Wrote 380 lines to pytorch_cuda_setup_notes.md | |
| # PyTorch CUDA Setup on NVIDIA GB10 (Blackwell) - Detailed Notes | |
| **Date:** October 13, 2025 | |
| **System:** ARM64 (aarch64) Linux with NVIDIA GB10 Grace Blackwell | |
| **Goal:** Install PyTorch with CUDA support to utilize the NVIDIA GB10 GPU | |
| --- | |
| ## System Environment | |
| … +370 lines (ctrl+o to expand) | |
| ● I've created comprehensive documentation in /home/dev/pytorch_cuda_setup_notes.md that covers: | |
| Contents | |
| System Environment | |
| - Complete hardware specs (ARM64, 20-core CPU, 119GB RAM) | |
| - NVIDIA GB10 GPU details (sm_121, Blackwell architecture, 119GB VRAM) | |
| - CUDA 13.0.1 toolkit components and libraries | |
| Challenges Encountered | |
| 1. Python venv setup (missing python3.12-venv package) | |
| 2. CPU-only PyTorch from default PyPI | |
| 3. Blackwell sm_121 compatibility issues with PyTorch 2.5.1 | |
| 4. No ARM64 CUDA nightly builds available | |
| The Solution | |
| - Why PyTorch 2.7.1 with CUDA 12.8 works | |
| - Step-by-step installation process | |
| - Package versions and sizes | |
| Verification | |
| - Complete test script | |
| - Results showing successful GPU operations | |
| - All operations verified (tensor ops, matrix multiplication) | |
| Key Takeaways | |
| - Version compatibility matrix | |
| - What worked vs what didn't | |
| - CUDA version compatibility notes | |
| Reference Section | |
| - Quick command reference | |
| - Troubleshooting guide | |
| - Requirements.txt for reproduction | |
| - Links to official documentation | |
| The document captures the entire journey from initial environment discovery through troubleshooting to the working solution, with | |
| enough detail that someone could reproduce this setup or understand why specific versions were needed. |
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Installed npm, then: