---
id: ai-frameworks
title: "AI Frameworks and Development Tools"
status: established
source_sections: "Web research: NVIDIA newsroom, Arm learning paths, NVIDIA DGX Spark User Guide"
related_topics: [dgx-os-software, gb10-superchip, ai-workloads]
key_equations: []
key_terms: [pytorch, nemo, rapids, cuda, ngc, jupyter, tensorrt, llama-cpp, docker, nvidia-container-runtime, fex]
images: []
examples: []
open_questions:
  - "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](https://learn.arm.com/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/)

### TensorRT (expected)
- NVIDIA's inference optimizer
- Blackwell architecture support expected

## 3. Development Environment

- **DGX Dashboard** — web-based system monitor with integrated JupyterLab (T0 Spec)
- **Python** — system Python with AI/ML package ecosystem
- **NVIDIA NGC Catalog** — library of pre-trained models, containers, and SDKs
- **Docker + NVIDIA Container Runtime** — pre-installed for containerized workflows (T0 Spec)
- **NVIDIA AI Enterprise** — enterprise-grade AI software and services
- **Tutorials:** https://build.nvidia.com/spark

## 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 run natively
- **FEX emulator** can translate x86 binaries to ARM at a performance cost (used for Steam/Proton gaming — see [[ai-workloads]])
- Container images must be ARM64/aarch64 builds

## Key Relationships

- Runs on: [[dgx-os-software]]
- Accelerated by: [[gb10-superchip]]
- Powers: [[ai-workloads]]