Market analysis

AI Infrastructure Landscape

Hyperscaler Dominance

Major tech giants like Google, Meta, and Microsoft control the majority of GPU resources, creating bottlenecks and high barriers to entry.

Alternative GPU Marketplaces

• Non-hyperscaler GPUs: TensorDock, Lambda, Vultr, VAST, Together, RunPod, ORI, Crusoe AI
• Distributed GPUs: DecentAI, Akash, Kuzco, Prime Intellect
• Tokenized + Distributed GPUs: GAIB, Compute Labs, Exabits, IOnet

Decentralized Physical Infrastructure Networks (DePIN)

Decentralized Physical Infrastructure Networks leverage blockchain technology to create distributed ownership models for physical computing resources. In AI computing, these models enable smaller players to compete with hyperscalers by aggregating resources, tokenizing GPU assets, and establishing decentralized governance—creating more accessible, cost-effective options for customers while giving token holders exposure to the growing AI infrastructure market. Silicon Network builds upon these DePIN principles while addressing limitations in quality control and enterprise reliability through a hybrid model that maintains the benefits of decentralized ownership.

AI Compute Demand Segments

Model Training

• Training large models requires massive GPU clusters (100-1000s of GPUs)
• Dominated by hyperscalers and well-funded AI companies
• $152B CapEx from Google, MSFT, Meta alone in 2024

Model Fine-tuning

• Requires medium-sized GPU clusters (1-8 GPUs)
• Critical for customizing models to specific applications
• Growing demand from enterprises and AI startups

Inference

• Can be efficiently run on distributed computing (1-2 GPUs)
• Offers the best price-performance ratio
• Inference infrastructure spending projected at $18.3B in 2024

Emerging GPU-Dependent Sectors

Beyond traditional AI model training and inference, GPU compute demand is rapidly expanding across diverse industries with specialized computational needs:

• Generative Media: Image generation (Stable Diffusion, DALL-E), video synthesis, and audio generation services require substantial GPU resources to process creation requests at scale
• 3D Rendering: Animation studios, architectural firms, and game developers leverage GPU acceleration for ray tracing and complex scene rendering
• Scientific Computing: Research in genomics, molecular dynamics, climate modeling, and quantum chemistry increasingly depends on GPU parallelization
• Financial Modeling: Quantitative analysis, risk assessment, and high-frequency trading algorithms utilize GPUs for real-time data processing
• Medical Imaging: Diagnostic tools employing advanced image processing and analysis for MRI, CT, and ultrasound interpretation
• Extended Reality: AR/VR content creation and real-time spatial computing applications demand significant rendering capabilities
• Cybersecurity: Threat detection systems, encryption/decryption operations, and cryptographic mining require specialized processing capacity

These sectors represent substantial growth vectors for GPU compute demand, complementing the core AI workloads and further diversifying utilization patterns across the Silicon Network infrastructure.