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id title status source_sections related_topics key_equations key_terms images examples open_questions
ai-frameworks AI Frameworks and Development Tools established Web research: NVIDIA newsroom, Arm learning paths [dgx-os-software gb10-superchip ai-workloads] [] [pytorch nemo rapids cuda ngc jupyter tensorrt llama-cpp] [] [] [TensorFlow support status on ARM GB10 (official vs. community) Full NGC catalog availability — which containers work on GB10? vLLM or other inference server support on ARM Blackwell JAX support status]

AI Frameworks and Development Tools

The Dell Pro Max GB10 supports a broad AI software ecosystem, pre-configured through DGX OS.

1. Core Frameworks

PyTorch

  • Primary deep learning framework
  • ARM64-native builds available
  • Full CUDA support on Blackwell GPU

NVIDIA NeMo

  • Framework for fine-tuning and customizing large language models
  • Supports supervised fine-tuning (SFT), RLHF, and other alignment techniques
  • Optimized for NVIDIA hardware

NVIDIA RAPIDS

  • GPU-accelerated data science libraries
  • Includes cuDF (DataFrames), cuML (machine learning), cuGraph (graph analytics)
  • Drop-in replacements for pandas, scikit-learn, and NetworkX

2. Inference Tools

CUDA Toolkit

  • Low-level GPU compute API
  • Compiler (nvcc) for custom CUDA kernels
  • Profiling and debugging tools

llama.cpp

  • Quantized LLM inference engine
  • ARM-optimized builds available for GB10
  • Supports GGUF model format
  • Documented in Arm Learning Path

TensorRT (expected)

  • NVIDIA's inference optimizer
  • Blackwell architecture support expected

3. Development Environment

  • Jupyter Notebooks — pre-installed for interactive development
  • Python — system Python with AI/ML package ecosystem
  • NVIDIA NGC Catalog — library of pre-trained models, containers, and SDKs
  • Containers — Docker/container support for reproducible environments

4. Software Compatibility Notes

Since the GB10 is an ARM system:

  • All Python packages must have ARM64 wheels or be compilable from source
  • Most popular ML libraries (PyTorch, NumPy, etc.) have ARM64 support
  • Some niche packages may require building from source
  • x86-only binary packages will not work

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