As AI models continue to evolve, the distinction between training and inference hardware becomes increasingly important. Understanding these differences, along with advances in memory technologies, will help determine whether GPU supply chain constraints will persist or if AI servers with specialized accelerators will dominate.
Training vs. Inference Hardware
- Training Phase: During training, GenAI models process large datasets by adjusting internal parameters, or weights. This involves forward passes (calculating outputs) and backward passes (updating weights based on errors), a process known as backpropagation. Training is resource-intensive, requiring significant memory to store intermediate results, parameters, gradients, and activations. GPUs with high-bandwidth memory (HBM) excel here, as they handle vast amounts of data while maximizing the parallel processing capabilities of GPUs.
- Inference Phase: Once trained, the model makes predictions on new data through a simpler forward pass. Inference is less computationally demanding and focuses on efficiency, low power consumption, and real-time analytics. It’s ideal for hardware such as FPGA chips or specialized AI servers, which are designed for high-efficiency tasks, such as autonomous driving or predictive maintenance.
Memory Technologies and Their Impact
Memory plays a key role in AI workloads, particularly for continuous learning models that require both high bandwidth and capacity.
- HBM (High-Bandwidth Memory): HBM is essential for handling large data volumes, making it the preferred choice for continuous learning models due to its ability to manage high data throughput during both forward and backward passes. Nvidia and AMD GPUs rely heavily on HBM for their high-performance needs.
- DRAM (e.g., DDR5): DRAM is commonly used during inference due to its high capacity and deterministic operations, often found in AI servers focusing on efficiency and real-time processing.
- Compute-in-Memory (CIM) and Processing-in-Memory (PIM): These emerging technologies present alternatives to traditional memory architectures. CIM integrates computation within memory, reducing data movement and increasing efficiency, while PIM enables simple operations to be performed directly in the memory, cutting down on external memory bandwidth needs.
Continuous Learning: The Shift in AI Workloads
Today, continuous learning, also known as online training, occurs during both training and inference phases. This shift requires hardware to evolve in parallel, driving demand for advanced GPUs with high memory bandwidth, efficient AI accelerators, and innovative memory technologies.
The Future of AI Hardware: More GPUs (Nvidia) or AI Servers (Groq) Over the Next 5 Years?
As the demand for AI grows, the question arises: Will we need more GPUs for both training and inference, or will specialized AI servers take the lead?
Specialized AI Servers and Memory Optimization
Companies such as NeuReality, d-Matrix, and Groq are developing AI hardware accelerators and servers optimized for inference. These systems focus on minimizing data movement and reducing reliance on large external memory like HBM, aiming for higher efficiency in AI processing.
Future Hardware Directions: GPUs vs. Specialized AI Servers
- GPUs (Nvidia, AMD, etc.): The demand for high-end GPUs with HBM will remain strong, especially in organizations that require hardware capable of handling both training and inference workloads. GPUs offer flexibility and precision, making them essential for tasks that require large-scale data processing.
- AI Servers: Companies developing specialized AI servers, such as d-Matrix and Groq, will capture a growing share of the inference market. These servers will increasingly use accelerators, including PIM and CIM technologies, to efficiently manage the memory and compute demands of continuous learning during inference. However, when it comes to training, GPUs with large HBM will continue to dominate due to their superior efficiency.
Prediction: Hybrid Systems
I foresee the rise of hybrid systems that integrate GPUs with specialized AI accelerators, leveraging the strengths of both. GPUs will continue to handle tasks that require flexibility and precision, while AI accelerators will efficiently manage specific inference workloads. This combination is likely to lead to the standardization of hardware for both training and inference applications.
Nvidia and other GPU manufacturers will remain in high demand with a tight supply chain for the newest chips, thanks to their adaptable software stacks and architectures that evolve to handle both training and inference simultaneously. Meanwhile, AI servers designed for specific use cases, where low power consumption and real-time performance are critical, will see increased adoption for real-time use cases during inference.