Key Facts and Data Points

  • First GPU: Nvidia launched the GeForce 256 in 1999, heralding the modern GPU era.
  • Market Share: Nvidia commands roughly 90% of the discrete PC GPU market, with AMD and Intel sharing the remainder.
  • Architecture: GPUs consist of thousands of simple cores optimized for massive parallel processing, unlike CPUs which have fewer, complex cores for sequential tasks.
  • Performance Example: A 1920×1080 display at 60 fps requires the GPU to update ~120 million pixels per second.
  • Software Ecosystem: CUDA (Compute Unified Device Architecture) is Nvidia’s proprietary platform enabling general‑purpose GPU computing, widely used in AI workloads.

Background and Context

  • Evolution: Initially designed for video‑game graphics, GPUs now underpin Artificial Intelligence (AI), Machine Learning (ML), and High‑Performance Computing (HPC) due to their parallelism.
  • Hardware Integration: GPUs can be discrete cards, integrated on the same die as the CPU (SoC), or embedded in smartphones and laptops.
  • Global Players: Apart from Nvidia, AMD and Intel are the other major manufacturers, but Nvidia leads in performance and ecosystem support.

Significance for India / Governance / Policy

  • Strategic Importance: AI and HPC are identified as critical enablers for sectors like defence, healthcare, climate modelling, and smart cities.
  • Government Initiatives:
  • IndiaAI Mission – aims to build indigenous AI capabilities, with GPU development as a key component.
  • India Semiconductor Mission & Mission 2.0 – focus on establishing a domestic semiconductor ecosystem, including design and fabrication of GPUs.
  • Economic Impact: Indigenous GPU design reduces dependence on imports, supports Make in India, and creates high‑skill jobs.

Related Constitutional / Legal Provisions

  • Article 246 (Union List) – Allows the Centre to legislate on electronics and information technology, providing a legal basis for national semiconductor policies.
  • National Policy on Electronics (2020) – Encourages R&D, manufacturing, and export of electronic components, including GPUs.

Frequently Asked Questions (FAQs)

  1. What is a GPU? A parallel‑processing semiconductor chip that executes thousands of simple computations simultaneously, ideal for graphics rendering and AI workloads.
  2. How does a GPU differ from a CPU? CPUs handle complex, sequential tasks with strong control logic; GPUs allocate many simple cores for parallel execution of repetitive mathematical operations.
  3. What is CUDA? Nvidia’s proprietary parallel‑computing platform that enables general‑purpose processing on GPUs, creating ecosystem dependence in AI development.

Previous Year Question (PYQ 2020) – With the present state of development, Artificial Intelligence can effectively do which of the following? (Answer: b – Bring down electricity consumption in industrial units, Create meaningful short stories and songs, Disease diagnosis).