Understanding the computational demands and bottlenecks of LLMs, as discussed previously, naturally leads to the question: where do these models actually run? The choice of hardware is not merely an implementation detail; it fundamentally shapes the feasibility, performance, and cost of deploying large language models. Different hardware platforms offer distinct profiles in terms of computational power, memory characteristics, energy efficiency, and programmability. Selecting the appropriate hardware, or optimizing a model for a specific target, is therefore a foundational aspect of LLM efficiency. Let's survey the main types of hardware relevant to LLM deployment.

Central Processing Units (CPUs)

CPUs are the ubiquitous general-purpose processors found in everything from laptops to servers. They are designed for flexibility, handling a wide variety of tasks sequentially or with limited parallelism.

Characteristics: Feature a small number of powerful cores optimized for low latency on individual tasks. They excel at control flow, integer operations, and tasks requiring complex logic. Modern server-grade CPUs often include vector instruction sets like AVX (Advanced Vector Extensions), which can provide some acceleration for numerical computations by performing operations on multiple data points simultaneously.
LLM Relevance: While CPUs can run LLMs, they typically struggle with the massive parallelism inherent in transformer models. Matrix multiplications and attention mechanisms involve vast numbers of independent calculations, which are better suited to more parallel architectures. Memory bandwidth on CPUs is also generally much lower than on specialized accelerators, often becoming the primary bottleneck for LLM inference, especially as model size increases.
Use Cases: Best suited for very small models, development and debugging (due to ease of use and debugging tools), or scenarios where inference latency is not the absolute priority and specialized hardware is unavailable or cost-prohibitive. They often handle orchestration and data preprocessing tasks even when inference runs on an accelerator.

Graphics Processing Units (GPUs)

Originally designed for rendering graphics, GPUs have become the workhorses of deep learning due to their massively parallel architecture.

Characteristics: Contain thousands of simpler cores compared to CPUs, optimized for high throughput on parallelizable workloads. They possess significantly higher memory bandwidth, often using specialized memory like HBM (High Bandwidth Memory), which is essential for feeding the numerous cores and handling the large parameter sets and activations of LLMs. Modern GPUs (like NVIDIA's A100, H100, or AMD's MI series) include specialized units, such as Tensor Cores, designed to accelerate mixed-precision matrix multiplications (e.g., FP16, BF16, INT8), directly benefiting deep learning performance.
LLM Relevance: GPUs offer a compelling balance of performance, programmability (via frameworks like CUDA and ROCm), and relatively mature software ecosystems (PyTorch, TensorFlow, optimized libraries like cuDNN, cuBLAS, TensorRT). They significantly accelerate both training and inference for most LLMs, addressing the compute and memory bandwidth bottlenecks identified earlier.
Use Cases: The standard choice for training large models and deploying performance-sensitive LLM inference services in data centers and cloud environments. The availability of different GPU tiers allows for some trade-offs between performance and cost.

Tensor Processing Units (TPUs)

TPUs are Application-Specific Integrated Circuits (ASICs) developed by Google explicitly for accelerating neural network computations.

Characteristics: Employ a systolic array architecture, which is highly optimized for large matrix multiplications. They are designed for high throughput and power efficiency on specific tensor operations. TPUs often excel with specific data types like bfloat16 and can handle very large batch sizes effectively. Interconnects between TPUs are also designed for large-scale distributed training.
LLM Relevance: Provide substantial performance for large-scale LLM training and inference, particularly when models and workloads align well with the TPU's strengths. They are a core part of Google's cloud AI infrastructure.
Use Cases: Primarily used within the Google Cloud Platform ecosystem for training foundational models and large-scale inference. While highly performant, their availability is restricted compared to GPUs, and the programming model (often involving XLA - Accelerated Linear Algebra) might require specific adaptation.

Other Accelerators: NPUs, FPGAs, and Custom ASICs

Beyond the main categories, a diverse range of specialized hardware is emerging:

Neural Processing Units (NPUs): Often found integrated into System-on-Chips (SoCs) for mobile phones, edge devices, and laptops. They are designed for power-efficient inference of smaller to medium-sized models directly on the device, enabling applications like real-time translation or content generation without relying on the cloud. Their capabilities vary widely.
Field-Programmable Gate Arrays (FPGAs): Offer hardware reconfigurability, allowing developers to create highly customized data paths optimized for specific model architectures or operations. This can yield high performance and efficiency but requires significant hardware design expertise (using Hardware Description Languages like Verilog or VHDL) and longer development cycles.
Custom ASICs: Companies beyond Google also develop ASICs tailored for AI/ML workloads. These aim for peak performance or efficiency for specific applications but lack the generality of CPUs or the programmability flexibility of GPUs. Examples include specialized chips for inference from startups and established hardware vendors.

Comparative Overview

Choosing the right hardware involves balancing performance needs, budget, power constraints, and the required software ecosystem flexibility. The following chart provides a qualitative comparison:

Relative comparison of hardware characteristics relevant to LLMs. 'Other' represents a broad category with significant variability (e.g., edge NPUs prioritize efficiency, high-end ASICs prioritize performance). Accessibility/Cost reflects general availability and typical price points, which can vary greatly within categories (especially GPUs and 'Other').

System-Level Considerations

For the largest LLMs, exceeding billions or even trillions of parameters, a single accelerator device (GPU or TPU) is often insufficient due to memory capacity limitations. Deploying these models necessitates distributed systems comprising multiple interconnected accelerators. High-speed interconnects like NVIDIA's NVLink or InfiniBand become critical for efficiently transferring activations and gradients between devices in multi-GPU setups. Effective deployment thus requires considering not just the individual accelerator but the entire system architecture, including networking, host CPU capabilities, and storage.

Understanding this hardware context is essential because the effectiveness of different compression and acceleration techniques (quantization, pruning, distillation, etc.) often depends heavily on the target hardware's capabilities. For instance, certain quantization formats might have direct hardware support, while the benefits of unstructured pruning depend on runtime support for sparse computations. As we proceed through this course, we will continually relate optimization techniques back to the underlying hardware platforms where they are deployed.