Introduction

The rapid evolution of artificial intelligence (AI) in 2025 has fueled a growing interest in running AI models locally on personal computers. This trend is driven by the need for enhanced privacy, reduced latency, and cost-effectiveness compared to cloud-based solutions. Small-format AI platforms, such as desktops and mini-PCs, are ideal for developers, researchers, students, and small businesses looking to prototype or deploy AI models like large language models (LLMs) or image generation tools without relying on heavy infrastructure. Advances in model optimization (e.g., quantization) and microservices like NVIDIA NIM have made it possible to run complex AI workloads on compact hardware.

This article provides a comprehensive overview of small-format AI platforms and GPUs available in 2025, focusing on NVIDIA’s offerings, competitor alternatives, and recommendations for various use cases.

Why Local AI Computation?

Local AI computation offers several advantages:

• Privacy: Keeping sensitive data on-site, avoiding cloud vulnerabilities.
• Low Latency: Faster processing without network delays.
• Cost Savings: Eliminating recurring cloud subscription costs.
• Accessibility: Optimized models and frameworks enable powerful AI on smaller devices.

These benefits make small-format platforms appealing for a wide range of users, from hobbyists experimenting with generative AI to professionals training complex models.

NVIDIA’s Small-Format AI Platforms

NVIDIA continues to lead the AI hardware market with innovative small-format platforms designed for local computation. Below are the key offerings as of June 2025.

DGX Spark

Announced at GTC 2025, the DGX Spark (NVIDIA DGX Spark), formerly known as Project DIGITS, is marketed as the world’s smallest AI supercomputer. It is designed for researchers, data scientists, robotics developers, and students.

Specifications

Feature	Details
Core Component	NVIDIA GB10 Grace Blackwell Superchip (CPU: 20 cores, 10 Cortex X-925 + 10 Cortex-A725; GPU: Blackwell)
AI Compute Performance	Up to 1 petaflop (1,000 trillion operations per second) for fine-tuning and inference
Memory	128 GB unified, up to 4 TB NVMe storage
Interconnect	NVLink-C2C (5x bandwidth of PCIe Gen 5)
Supported Models	NVIDIA Cosmos Reason, GR00T N1 robot foundation model
Platform Integration	NVIDIA full-stack AI platform, scalable to DGX Cloud
Manufacturers	ASUS, Dell, HP Inc., Lenovo
Price	~$3,000 (based on X posts, subject to confirmation)

Features

• Compact Design: Comparable in size to a Mac Mini, ideal for desktop use.
• Energy Efficiency: Optimized for low power consumption.
• Software Support: Compatible with TensorFlow, PyTorch, Llama.cpp, and NVIDIA NIM microservices (e.g., ChatRTX, ComfyUI).
• Use Cases: Running generative AI models up to 200 billion parameters, such as LLMs or Stable Diffusion.

The DGX Spark is a game-changer for local AI, offering supercomputing power in a compact, accessible package.

DGX Station

For users requiring more power, the DGX Station is a high-performance desktop solution for complex AI workloads.

Specifications

Feature	Details
Core Component	GB300 Grace Blackwell Ultra Desktop Superchip
AI Compute Performance	20 petaflops (20,000 TOPS)
Memory	784 GB
Use Cases	Training and inference of large-scale AI models

Features

• High Performance: Suitable for professional-grade AI tasks, including training large models.
• Scalability: Integrates with NVIDIA’s ecosystem for seamless cloud transitions.
• Limitations: Larger and likely more expensive than DGX Spark.

Jetson TX2

While primarily for embedded systems, the NVIDIA Jetson TX2 is sometimes used in desktop configurations for lightweight AI tasks.

Specifications

Feature	Details
Performance	>1 teraflop
Size	Credit card-sized module
Power Consumption	<10 W
Use Cases	IoT, robotics, drones

Features

• Compact and Efficient: Ideal for low-power applications.
• Limitations: Not suited for large LLMs or heavy AI workloads.

GPUs for Local AI Computation

For users building custom AI desktops, GPUs are critical for accelerating AI workloads. NVIDIA’s GeForce RTX series, particularly the new RTX 50 series based on the Blackwell architecture, leads the market.

NVIDIA GeForce RTX 5090

The RTX 5090 (RTX 5090) is NVIDIA’s flagship consumer GPU in 2025, offering unmatched performance for AI tasks.

Specifications

Feature	Details
CUDA Cores	21,760
Tensor Cores	5th Generation, 3,352 AI TOPS
Ray Tracing Cores	4th Generation, 318 TFLOPS
Memory	32 GB GDDR7, 512-bit interface
Boost Clock	2.41 GHz
Power	575 W
Price	~$2,000+

Features

• AI Performance: Supports large LLMs (e.g., LLaMA 70B) and image generation (e.g., Stable Diffusion).
• Technologies: DLSS 4 with Multi Frame Generation, NVIDIA Reflex 2, full ray tracing with neural rendering.
• Software Support: CUDA, TensorRT, NVIDIA Broadcast, and Studio Drivers.
• Use Cases: Ideal for researchers and developers running complex AI models locally.

Other NVIDIA GPUs

• RTX 4090: A previous-generation GPU with 24 GB GDDR6X memory, still excellent for AI tasks (~$2,000).
• RTX 4060 Ti (16 GB): Budget-friendly option for smaller models like Mistral 7B or LLaMA 13B (~$500).
• Quadro RTX 8000: Professional-grade GPU with 48 GB memory (expandable to 96 GB via NVLink), suited for training and rendering (~$10,000).

Competitors and Alternatives

While NVIDIA dominates small-format AI platforms, competitors offer GPUs and chips for AI, though none provide direct equivalents to DGX Spark or DGX Station.

AMD

• Radeon RX 7900 XTX:
  • VRAM: 24 GB
  • Software: ROCm (less mature than CUDA)
  • Use Cases: Suitable for AI with PyTorch, but less optimized than NVIDIA GPUs
  • Price: ~$1,000
  • Limitations: Limited framework support and community adoption (AMD ROCm).

Intel

• Arc B580 (Best Graphics Cards):
  • VRAM: Up to 12 GB
  • Software: DirectML
  • Use Cases: Lightweight AI inference
  • Price: ~$350
  • Limitations: Lower AI performance compared to NVIDIA.
• Gaudi3: Designed for data centers, not desktops.

Apple

• M4 Chip:
  • Description: Integrated GPU in Apple Silicon, optimized via Metal.
  • Use Cases: Lightweight LLMs (e.g., Mistral 7B) on MacBook or Mac Mini.
  • Advantages: Energy-efficient, seamless in Apple ecosystem.
  • Limitations: Not suitable for heavy AI workloads.

Startups

• Groq: Offers Language Processing Units (LPUs) for efficient AI inference, but not widely available in desktop formats.
• Cerebras and Graphcore: Focus on data center solutions, not small-format platforms.

Choosing the Right Hardware

Selecting the right platform or GPU depends on your use case, budget, and technical requirements. Key considerations include:

• VRAM: Essential for large models:
  • 8 GB: Small models (Mistral 7B, LLaMA 7B quantized)
  • 12-16 GB: Medium models (LLaMA 13B, Stable Diffusion)
  • 24 GB+: Large models (LLaMA 70B, training)
• AI Compute Performance: Measured in TOPS (e.g., RTX 5090: 3,352 TOPS).
• Software Support: NVIDIA’s CUDA and TensorRT are industry standards, offering better compatibility with AI frameworks.
• Budget: Ranges from $300 (RTX 3060) to $10,000+ (Quadro RTX 8000, DGX Station).
• System Requirements:
  • CPU: Intel Core i7/i9 or AMD Ryzen 7/9
  • RAM: 32 GB minimum, 64 GB for training
  • Storage: 1 TB+ NVMe SSD
  • Cooling: Essential for high-end GPUs

Recommendations by Use Case

User Type	Recommended Hardware	Use Case
Beginners/Hobbyists	RTX 3060 (12 GB, ~$300) or RTX 4060 Ti (16 GB, ~$500)	Small models (Mistral 7B, Stable Diffusion)
Researchers/Developers	RTX 5090 (32 GB, $2,000) or DGX Spark ($3,000)	Medium to large models (LLaMA 70B, Mixtral)
Professionals/SMEs	Quadro RTX 8000 (48 GB, ~$10,000) or DGX Station	Training large models, 3D rendering
Apple Ecosystem	Mac Mini/MacBook Pro with M4	Lightweight AI tasks

Trends and Outlook for 2025

• NVIDIA’s Dominance: CUDA, TensorRT, and NIM microservices solidify NVIDIA’s lead in local AI.
• Emerging Competition: AMD’s ROCm and Intel’s Gaudi3 are improving, but lag in desktop solutions.
• Open-Source Growth: Tools like Llama.cpp and Ollama make local AI more accessible.
• Energy Efficiency: Blackwell-based platforms offer high performance per watt.
• Software Optimization: Quantization (4-bit, 8-bit) reduces hardware requirements for large models.

Conclusion

In 2025, NVIDIA leads the market for small-format AI platforms with the DGX Spark and DGX Station, offering compact, powerful solutions for local AI computation. The RTX 5090 GPU, with its 32 GB GDDR7 memory and 3,352 AI TOPS, is the top choice for custom AI desktops. While competitors like AMD, Intel, and Apple provide alternatives, they lack dedicated small-format platforms and robust software ecosystems. For hobbyists, researchers, or professionals, NVIDIA’s hardware and software integration make it the go-to choice for local AI in 2025.