Artificial Intelligence (AI) infrastructure providers are essential to the development and deployment of AI models, offering specialized hardware and cloud-based solutions. Each provider has unique strengths and weaknesses, making them suitable for different use cases. Below, we examine key AI infrastructure providers, their advantages, and their limitations.
NVIDIA
Advantages:
- Deep knowledge of its own architecture, allowing for optimization at the PTX layer.
- Large blocks of High Bandwidth Memory (HBM) provide better performance than TPUs or ASICs by enabling individual GPUs to store more data close to CUDA cores.
- NVLink and NVSwitch enable fast inter-chip communication, improving overall performance.
Disadvantages:
- Heavy reliance on CUDA, which may become a challenge if alternative chipsets gain traction in the market.
TPUs (Google)
Advantages:
- Can compensate for inefficiencies via software-level scheduling across chips, especially when aware of network topology.
- Prioritizes energy efficiency and large batch sizes, which may be beneficial for certain workloads.
Disadvantages:
- Generally features smaller chips with more distributed memory, which could impact performance in certain applications.
AMD
Advantages:
- Strong potential for competition if it closely collaborates with customers, offering support and training to integrate workloads with its hardware.
Disadvantages:
- To compete effectively, AMD and other newer competitors must invest significant time and resources in working closely with customers, providing support and training. This could divert focus from core innovation on their existing technology stack.
Together AI
Advantages:
- Specializes in fast inference with some of the best implementations of open-source models.
- Provides clean documentation, easy-to-follow demonstrations, and a good developer experience.
- Expanding its reach by building its own data centers.
Disadvantages:
- Primarily focused on inference rather than broader AI training, though custom fine-tuning is available.
Databricks
Advantages:
- Well-integrated for enterprises already using Databricks instances, making data integration seamless.
Disadvantages:
- Not as competitive in achieving top inference latency compared to providers like Together AI or Fireworks.
Google Cloud Platform (GCP)
Advantages:
- Strong offerings in accelerated cloud computing.
Disadvantages:
- May prioritize internal customers (such as Google itself) before external clients.
AWS
Advantages:
- Well-adopted industry standard, with many customers familiar with its ecosystem.
- Engages extensively with customers to understand and build relevant solutions.
Azure
Advantages:
- Claims to understand next-generation AI workloads through its partnership with OpenAI.
CoreWeave
Advantages:
- More focused on financing data center acquisition and developing software to maximize utilization rather than building its own models or inference endpoints.
Disadvantages:
- Primarily sells GPUs rather than offering inference as a service, which may limit its appeal for customers looking for a more complete AI infrastructure solution.
Additional Considerations in AI Infrastructure
Memory Bandwidth
- Memory bandwidth remains a significant limiting factor, particularly for deploying Mixture of Experts (MoE) models.
Test-Time Compute
- There is a growing trend toward prioritizing test-time compute, as it offers a better cost-performance ratio.
Kernel Efficiency
- While kernel efficiency is important, end-to-end performance and the ability to fully utilize GPUs or TPUs are more critical for profitability.
Data Centers
- The colocation of data and proximity to customers are essential for minimizing latency and improving performance.
Conclusion
Choosing the right AI infrastructure provider depends on specific business needs, whether it’s maximizing inference speed, optimizing training efficiency, or integrating seamlessly with existing enterprise solutions. As the AI industry evolves, providers will continue to refine their offerings to stay competitive in this fast-moving landscape.