By Sagar Shankaran, Founder of CallSphere
Understand why GPUs dominate AI workloads, how their massively parallel architecture maps to neural network math, and what high-bandwidth memory means for model training and inference.
Key takeaways
Central processing units are engineering marvels optimized for sequential task execution. A modern CPU core can handle extraordinarily complex branching logic, speculative execution, and out-of-order processing. But training a neural network does not require complex branching logic. It requires performing the same simple mathematical operation — multiply two numbers and add the result to a running sum — trillions of times.
This is the fundamental insight behind the GPU revolution in AI. Graphics processing units were originally designed to calculate pixel colors for video games, a task that requires performing the same shading calculation independently for millions of pixels simultaneously. Neural network training requires performing the same matrix multiplication independently for millions of weight updates simultaneously. The computational pattern is nearly identical.
A contemporary high-end AI accelerator contains architectural elements that would be unrecognizable to someone familiar only with CPU design.
flowchart TD
MSG(["New message"])
WORKING["Working memory<br/>rolling window"]
EPISODIC[("Episodic memory<br/>past sessions")]
SEMANTIC[("Semantic memory<br/>facts and preferences")]
SUM["Summarizer<br/>compresses old turns"]
ROUTER{"Retrieve<br/>needed memories"}
PROMPT["Assembled context"]
LLM["LLM"]
UPD["Memory updater<br/>writes new facts"]
MSG --> WORKING --> ROUTER
ROUTER -->|Past sessions| EPISODIC
ROUTER -->|User facts| SEMANTIC
EPISODIC --> SUM --> PROMPT
SEMANTIC --> PROMPT
WORKING --> PROMPT --> LLM --> UPD
UPD --> EPISODIC
UPD --> SEMANTIC
style ROUTER fill:#4f46e5,stroke:#4338ca,color:#fff
style LLM fill:#f59e0b,stroke:#d97706,color:#1f2937
style EPISODIC fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b
style SEMANTIC fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b
The core compute unit is the streaming multiprocessor (SM), a self-contained processing block containing dozens of smaller execution units. A flagship AI accelerator contains over 100 SMs, each capable of executing thousands of threads simultaneously. The total thread count — often exceeding 100,000 concurrent threads — dwarfs what any CPU can achieve.
Each SM contains specialized hardware for different precision levels:
Tensor cores represent the single most important architectural innovation for AI workloads. A standard FP32 core performs one multiply-add operation per clock cycle. A tensor core performs a 4x4 matrix multiply-accumulate in a single operation — effectively 128 multiply-add operations per cycle.
The throughput difference is staggering:
| Precision Format | Operations Per Second (Flagship Accelerator) |
|---|---|
| FP32 (standard) | ~60 TFLOPS |
| FP16 (half precision) | ~500 TFLOPS |
| FP8 (quarter precision) | ~1,000 TFLOPS |
| INT8 (integer) | ~1,000 TOPS |
This 16x difference between FP32 and FP8 throughput is why the AI industry has invested so heavily in developing training techniques that work at reduced precision.
The other critical bottleneck is memory bandwidth. It does not matter how fast your compute units are if you cannot feed them data quickly enough. Modern AI accelerators use High-Bandwidth Memory (HBM) — a technology where multiple DRAM dies are stacked vertically and connected through thousands of microscopic wires called through-silicon vias (TSVs).
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Current-generation HBM provides:
Compare this to a high-end CPU with DDR5 memory: roughly 100 GB/s bandwidth. The 20-50x bandwidth advantage of HBM is what makes large-model inference feasible at interactive speeds.
Neural network training decomposes naturally into parallel operations at multiple levels.
The simplest form: replicate the entire model across multiple accelerators, give each copy a different subset of training data, and average the resulting gradient updates. If you have 1,000 accelerators, you can process 1,000 batches simultaneously, reducing training time by nearly 1,000x.
The catch is communication overhead. After each batch, every accelerator must share its gradients with every other accelerator through an all-reduce operation. This is where high-speed interconnects become critical — the all-reduce step can dominate total training time if network bandwidth is insufficient.
When a model is too large to fit in a single accelerator's memory, you split it across multiple devices. There are two sub-approaches:
Tensor parallelism splits individual matrix operations across accelerators. A matrix multiplication that produces a 16,384-dimensional output can be split across 8 accelerators, each computing 2,048 dimensions. This requires extremely high-bandwidth connections because partial results must be combined at every layer.
Pipeline parallelism assigns different layers of the model to different accelerators. Accelerator 1 handles layers 1-10, Accelerator 2 handles layers 11-20, and so on. Data flows through the pipeline like an assembly line. The challenge is pipeline bubbles — idle time while accelerators wait for their input from the previous stage.
Mixture-of-experts (MoE) architectures introduce a third dimension of parallelism. The model contains many "expert" sub-networks, but only a few activate for any given input token. Each accelerator can host a subset of experts, and a lightweight routing mechanism directs tokens to the appropriate device. This enables models with trillions of parameters while keeping per-token compute costs manageable.
Despite the impressive bandwidth of HBM, memory remains the primary bottleneck for many AI workloads. This is known as the "memory wall" problem.
Consider inference with a large language model. Generating each output token requires reading the entire model's weights from memory. For a 70-billion parameter model stored in FP16, that means reading 140 GB of data for every single token generated. At 3 TB/s memory bandwidth, reading the full model takes roughly 47 milliseconds — setting a hard floor on generation latency regardless of compute speed.
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This arithmetic reveals why techniques that reduce effective model size — quantization, pruning, distillation — are so valuable for inference. A model quantized to 4-bit precision requires reading only 35 GB per token, cutting memory-bound latency by 4x.
Raw hardware parallelism is useless without software that can express and manage it. The AI software stack has evolved through several generations:
Low-level libraries provide optimized implementations of core operations. These include BLAS libraries for matrix operations, convolution libraries, and communication libraries for multi-accelerator coordination. They are written by hardware vendors and tuned to squeeze maximum throughput from specific architectures.
Compiler frameworks translate high-level neural network descriptions into optimized GPU kernels. Graph compilers analyze the entire computation graph and fuse operations, eliminate redundant memory copies, and schedule work to maximize accelerator utilization.
Training frameworks like PyTorch and JAX provide the researcher-facing API. They handle automatic differentiation, distributed training orchestration, checkpointing, and mixed-precision training. Modern frameworks can automatically apply data, tensor, and pipeline parallelism with minimal code changes.
The GPU's dominance in AI is not guaranteed forever. Several alternative architectures are competing for AI workloads:
However, the GPU ecosystem benefits from enormous software momentum. Millions of developers know how to program GPUs. Thousands of optimized libraries exist. The tooling, debugging, and profiling infrastructure is mature. Any challenger must overcome not just a hardware performance gap but an entire ecosystem gap.
Understanding GPU architecture is not just academic knowledge — it directly affects practical decisions:
The GPU revolution transformed AI from a research curiosity into an industrial force. Understanding the hardware is essential for anyone building AI systems at scale.
GPUs excel at AI workloads because they contain thousands of smaller cores designed for parallel processing, compared to a CPU's handful of powerful sequential cores. A modern AI accelerator can execute tens of thousands of simultaneous multiply-accumulate operations per clock cycle, making it up to 100x faster than a CPU for the matrix mathematics that underpin neural network training. This massive parallelism is what makes training models with billions of parameters feasible within days rather than years.
Tensor cores are specialized processing units within modern GPUs that are purpose-built to accelerate matrix multiplication operations — the fundamental mathematical operation in deep learning. They can perform mixed-precision matrix multiply-and-accumulate calculations in a single clock cycle, delivering 2-8x speedups over standard GPU cores for AI training tasks. Tensor cores have become so important that modern AI accelerator designs dedicate the majority of chip area to them.
GPU memory has evolved from standard GDDR to High Bandwidth Memory (HBM), which stacks multiple memory layers vertically to deliver bandwidth exceeding 3 terabytes per second on the latest accelerators. This evolution was essential because large language models with hundreds of billions of parameters require both massive capacity and extreme bandwidth to keep the compute cores fed with data. Modern AI accelerators now ship with 80-192 GB of HBM, and memory capacity remains one of the primary constraints on model size.
While AI models can technically run on CPUs, the performance difference makes CPU-only deployment impractical for most production workloads. Training a model that takes one day on a modern GPU cluster would take months or years on equivalent CPU hardware. Alternative accelerators like TPUs, custom ASICs, and FPGAs offer competition to GPUs, but the GPU ecosystem benefits from millions of trained developers and thousands of optimized software libraries that challengers must overcome.
Written by
Sagar Shankaran· Founder, CallSphere
LinkedInSagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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