What is GPU Computing? — AI Glossary

GPU computing is the use of graphics processing units (GPUs) to accelerate computational tasks — particularly the massive parallel matrix operations that train and run modern AI models. Originally designed to render video game graphics, GPUs turned out to be extraordinarily well-suited for deep learning because both tasks require performing millions of simple math operations in parallel. Today, GPUs are the essential hardware of AI: training GPT-4 reportedly used tens of thousands of NVIDIA A100 GPUs running for months.

CPUs vs. GPUs: Why AI Needs GPUs

The key difference between CPUs (central processing units) and GPUs comes down to core count and design philosophy:

• CPU: Typically 8–64 powerful cores, optimized for sequential tasks. Excellent at complex logic, running operating systems, handling diverse tasks one at a time.
• GPU: Thousands of smaller, simpler cores (NVIDIA’s H100 has 18,000+), optimized for doing the same operation on many data points simultaneously — exactly what matrix multiplication requires.

Training a neural network is fundamentally a process of repeated matrix multiplications and additions across billions of parameters. A CPU handles these sequentially; a GPU handles them in massive parallel batches. The speedup can be 10–100x depending on the workload.

This GPU advantage is why the modern AI boom was enabled, in part, by NVIDIA’s CUDA platform (launched 2006) — a programming model that let researchers use GPUs for general computation, not just graphics. Jensen Huang’s timing of that bet is now legendary in tech history.

The GPU Supply Chain and AI Race

GPU computing has become a geopolitical issue. NVIDIA dominates the AI GPU market with an estimated 70-80% market share for high-performance AI training chips. The H100 and H200 GPUs (released 2022-2023) became so critical to AI development that they were put under US export controls to China in 2023.

Competitors are emerging:

• AMD: Their MI300X chips are competitive with H100 for some workloads, with better HBM memory.
• Google TPUs: Custom tensor processing units used in Google’s own AI training, available via Google Cloud.
• AWS Trainium/Inferentia: Amazon’s custom chips for training and inference respectively.
• Intel Gaudi: A competitor in the training accelerator space.

All of this AI infrastructure runs on the same fundamental principle: massive parallelism through specialized hardware.

GPU Computing for Developers and Businesses

You don’t need to buy your own GPUs to use GPU computing. Cloud providers offer GPU access on-demand:

• Google Colab: Free tier includes NVIDIA T4 GPUs, sufficient for many experiments.
• AWS, GCP, Azure: Offer NVIDIA A100/H100 instances by the hour.
• Lambda Labs, CoreWeave, RunPod: GPU-focused cloud providers often 30-50% cheaper than major clouds.
• Hugging Face Spaces: Free GPU-backed hosting for demos and small models.

For running AI models locally, consumer GPUs (NVIDIA RTX 4090, for example) can run many open-source models like Llama 3 70B using quantization techniques that reduce memory requirements. Understanding GPU VRAM (video RAM) is key: a model needs roughly 2 bytes of VRAM per parameter in FP16 format — so a 7B parameter model needs ~14GB VRAM minimum.

10 Things to Know About GPU Computing in 2026

• H100s and B200s drive most production AI training. Nvidia hardware is the dominant frontier, but supply has loosened from the 2023-2024 squeeze.
• AMD MI300X is competitive for inference. AMD GPUs are gaining production-AI mindshare, especially for inference workloads.
• Apple Silicon (M-series) is surprisingly capable. Mac M-series with 64GB+ unified memory runs serious local AI models.
• Inference vs training have different GPU profiles. Training needs raw FLOPS; inference needs memory bandwidth and latency. The right GPU for each is different.
• Cloud GPU pricing has dropped meaningfully. A100 pricing on Lambda Labs, RunPod, and others fell ~30 percent in 2025.
• Quantization makes consumer hardware viable. 4-bit and 8-bit quantization shrinks model memory needs dramatically; consumer GPUs run 70B models comfortably.
• The CUDA moat is narrower but still real. AMD ROCm has caught up substantially, but CUDA ecosystem maturity still matters for many workloads.
• Compute-per-dollar matters more than peak FLOPS. For most workloads, sustained compute-per-dollar across your real usage pattern is the right metric, not peak benchmarks.
• Power and cooling become real constraints at scale. Data-center GPU deployment is now bottlenecked by grid capacity in many regions.
• Custom silicon is emerging. Google TPUs, Amazon Trainium, Microsoft Maia, OpenAI custom silicon all chip away at Nvidia dominance.

Key Takeaways

• GPUs accelerate AI by performing massive parallel matrix operations thousands of times faster than CPUs.
• NVIDIA dominates the AI GPU market; AMD, Google TPUs, and custom cloud chips are alternatives.
• Training large AI models requires tens of thousands of GPUs running for weeks or months.
• Cloud GPU rental makes powerful GPU computing accessible to anyone without owning hardware.
• For running models locally, VRAM is the key constraint — roughly 2GB per billion parameters in FP16.

Frequently Asked Questions

Why can’t CPUs replace GPUs for AI?

CPUs are optimized for fast, sequential, complex tasks. Training a neural network requires running the same simple operations (multiply-add) billions of times in parallel — GPUs have thousands of cores specifically designed for this. A CPU would take 10-100x longer for the same training job.

What GPU do I need to run AI models at home?

For small to medium models (7B-13B parameters), an NVIDIA RTX 3090 (24GB VRAM) or RTX 4090 (24GB VRAM) is a strong choice. With quantization (INT4), a 70B parameter model can run on 40-48GB of VRAM — achievable with two GPUs. Mac M-series chips with unified memory are also surprisingly capable for local inference.

What is VRAM and why does it matter?

VRAM (Video RAM) is the memory on your GPU. For AI inference, the entire model must fit in VRAM. If the model is too large, the GPU can’t load it (or performance degrades severely). VRAM is the primary constraint when choosing a GPU for AI work.

Is NVIDIA the only option for AI GPUs?

NVIDIA is dominant, but not the only option. AMD ROCm supports many popular AI frameworks, though compatibility is less universal than CUDA. For cloud users, Google TPUs and AWS custom chips are cost-effective for specific workloads. Apple Silicon (M-series) is excellent for local inference.

What’s the difference between training compute and inference compute?

Training compute is used to create the model (one-time, massive cost). Inference compute is used every time someone uses the model (ongoing, at scale). Different hardware is often optimal for each: H100s for training, more efficient chips like NVIDIA L4 or AWS Inferentia for inference.

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Sources

• Grokipedia
• Stanford HAI AI Index Report

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Sources

This article draws on official documentation, product pages, and industry reporting. Specific sources are linked inline throughout the text.

Last reviewed: April 2026