LLMs & Computer Engineering
HW Bytes Post #10: Personal takeaways from a wide range of articles.
Andrew Ng said: "AI is the new electricity."
I think we can unanimously agree with that.
And Andrej Karpathy beautifully explained in one of his recent seminars how that statement is so true.
Electricity grids need two types of investments: Capital Expenditures and Operational Expenditures.
And so do Large Language Models.
Capital Expenditures are the investments in long-term infrastructure required to train and serve large models.
Examples in LLM training:
Hardware purchases: Buying GPUs (like NVIDIA A100s or H100s), TPUs, or custom silicon (e.g., AWS Trainium).
Data center build-outs: Setting up on-premise data centers for LLM training.
Operational Expenditures are recurring costs associated with training, fine-tuning, serving, and maintaining the LLM.
Examples in LLM training:
Cloud compute costs: Paying for GPU/TPU time on AWS, Azure, Google Cloud, etc.
Personnel salaries: ML engineers, researchers, DevOps, MLOps, and data scientists.
Electricity is supplied and metered per unit watt; LLMs are supplied and metered per unit token.
LLM models demand low latency and consistent quality just like Electricity.
Similar to transfer switches that switch between grid, solar, and battery, we have OpenRouters that can help us switch between different LLM models like OpenAI, Grok, etc.
A recent study also showed that when many LLMs went down, people were stuck and unable to work, which scientists call "intelligence brownouts"—similar to electricity blackouts.
Now we have established that LLMs are ubiquitous. But there is another striking resemblance between LLMs and the semiconductor industry itself.
All major semiconductor players have two options: either fabricate their own chips, just like Intel and Samsung do, or rely on external companies like TSMC, as Apple does.
Similarly, anyone training on NVIDIA GPUs can be considered a fabless model. On the other hand, Google training on their Tensor Processing Units can be equated to Intel owning a fab.
But either way, there is no AI without the underlying hardware, and day by day, AI is dictating how the future of hardware should look.
When mobile phones and laptops came into the market, they gave birth to a wide range of specifications for SoCs. Now it's AI's time.
What are AI workload requirements?
1. Enhanced Computational Power:
CPUs are capable of handling general-purpose tasks and managing the overall operation of AI systems, but not the intensive computation itself.
We need GPUs for specialized parallel processing that are ideal for training deep learning models. For example, NVIDIA's GPUs are widely used because of their efficiency in handling large-scale parallel processing.
As a engineer, having foundational knowledge of GPU architecture is essential.
2. High-Speed Memory:
Two main types of memory are used in AI systems:
Random Access Memory (RAM): Provides fast access to data that the processor is currently using.
Storage (like solid-state drives): Preferred for AI workloads due to their faster data access speeds compared to traditional hard drives.
Example: High-performance AI systems often use large amounts of DDR4 or DDR5 RAM to ensure smooth processing of complex tasks.
3. High-Performance Interconnects:
Interconnects are the communication pathways that transfer data between different components of an AI system. Efficient interconnects are essential for minimizing latency and maximizing data throughput.
Example: PCIe 4.0 provides twice the data transfer rate of PCIe 3.0, significantly improving the performance of AI systems that rely on fast data movement.
4. Robust Power Supply:
AI hardware requires robust power supply units (PSUs) to ensure stable and reliable operation.
Example: A high-end AI workstation might require a 1000W PSU to support multiple GPUs and high-performance CPUs.
This high energy consumption makes the processors extremely hot.
5. Advanced Cooling Systems:
Cooling systems are vital for maintaining optimal operating temperatures and preventing overheating in AI hardware.
Effective cooling solutions include:
Air Cooling: Uses fans and heatsinks to dissipate heat. Cost-effective but may struggle with high heat loads.
Liquid Cooling: Uses liquid to transfer heat away from components. Provides more efficient cooling and is quieter than air cooling.
Hybrid Cooling: Combines air and liquid cooling for enhanced performance.
High-performance AI servers often use liquid cooling systems to manage the heat generated by powerful GPUs and CPUs.
Summary: As modern AI workloads increase in complexity, the demand for sophisticated and powerful hardware components increases proportionally.