GPU Guide · Part 2 of 3 | July 1, 2026

Inside a GPU: CUDA Cores, Tensor Cores, Ray Tracing & VRAM – Explained in Simple English

The Big Picture: A GPU is Like a Factory

Imagine a GPU as a big factory with different departments. Each department has a specific job. When you play a game or edit a video, the work is divided among these departments. The more workers in a department, the faster the work gets done.

NVIDIA's GPUs have three main types of workers (or "cores"): CUDA Cores, Tensor Cores, and RT Cores. AMD uses different names for similar things. Let us look at each one.

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CUDA Cores – The General Workers

CUDA stands for Compute Unified Device Architecture. But forget the name. Just think of CUDA cores as general-purpose workers in the GPU factory. They can do any kind of simple math — adding numbers, multiplying, comparing values. Most of the work in games and applications is done by these cores.

More CUDA cores = faster performance — but only when comparing within the same generation. You cannot compare CUDA core count between NVIDIA and AMD because AMD uses a different architecture (their cores are called "Stream Processors" and work differently). Even within NVIDIA, a newer card with fewer CUDA cores can be faster than an older card with more cores because each core is individually more powerful.

Who should care about CUDA cores?

• Gamers: More CUDA cores generally means higher FPS in games, but clock speed and architecture matter too.
• Video editors using Premiere Pro: CUDA (NVIDIA) acceleration is much faster than AMD in most Adobe software.
• 3D artists: Blender, Octane, and Redshift all use CUDA for rendering.
• AI/ML people: CUDA is the standard. Most AI frameworks only work properly with NVIDIA's CUDA.

Tensor Cores – The AI Specialists

Tensor cores are specialized workers that only do one thing: AI math. They are incredibly fast at multiplying matrices (which is what AI models do most of the time).

NVIDIA introduced Tensor cores in 2018 with the RTX 2000 series. A Tensor core can do AI calculations 4x to 16x faster than a regular CUDA core. They are the reason NVIDIA dominates the AI market today.

What uses Tensor cores?

• DLSS (Deep Learning Super Sampling): NVIDIA's smart upscaling technology. The game renders at a lower resolution (say 1080p), then Tensor cores use AI to upscale it to 4K quality. You get 4K visuals with 1080p performance. Magic? Kind of.
• AI image generators: Stable Diffusion, Midjourney — these use Tensor cores heavily.
• AI video editing: Features like "remove background," "auto-reframe," and "noise reduction" in Premiere Pro and DaVinci Resolve use Tensor cores.
• Windows AI features: Windows 11 uses Tensor cores for background blur in video calls, voice typing, and other AI features.

RT Cores – The Light Simulators

RT stands for Ray Tracing. RT cores are specialized workers that calculate how light behaves — where shadows fall, how reflections work, how light bounces off surfaces.

Before RT cores, games used "fake" lighting tricks called rasterization. It looked okay, but not realistic. RT cores allow games to calculate real lighting, making scenes look like Hollywood movies.

The problem? Ray Tracing requires huge computational power. Even with RT cores, turning on Ray Tracing in a game can cut your FPS in half. That is why DLSS is important — it uses Tensor cores to restore the lost performance.

Do you need Ray Tracing? Honestly, for most people in 2026? It is nice but not necessary. Many gamers prefer higher FPS over fancy lighting. But as games increasingly support Ray Tracing by default, having RT cores will become more important over the next 2-3 years.

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The Alphabet Soup: RTX, GTX, RX — What Do They Mean?

This is one of the most confusing things for beginners. Let me break it down simply.

NVIDIA Naming

Name	Stands For	What It Means
GT	Giga Texel	Very old, low-end cards. You won't see these anymore. Avoid.
GTX	Giga Texel eXtreme	Mid-range to high-end cards without Tensor cores or RT cores. Examples: GTX 1660, GTX 1080 Ti. Good for gaming, but cannot do Ray Tracing or DLSS.
RTX	Ray Tracing Texel eXtreme	Cards with Tensor cores and RT cores. Examples: RTX 2060, RTX 3060, RTX 4090. These can do Ray Tracing, DLSS, and AI features.

The number after RTX/GTX: The first two digits are the generation, the last two digits are the performance level.

• RTX 3050: 30 = 3rd gen RTX (3000 series), 50 = entry-level
• RTX 3060: 60 = mid-range
• RTX 3070: 70 = high mid-range
• RTX 3080: 80 = high-end
• RTX 3090/4090: 90 = flagship (most expensive, most powerful)

Ti, Super, and other suffixes: "Ti" or "Super" means a slightly faster version of the same card. Example: RTX 3070 Ti is faster than RTX 3070.

AMD Naming

Name	Stands For	What It Means
RX	Radeon eXtreme	This is AMD's brand name for their GPUs. All modern AMD cards start with RX.
Radeon	Brand name	AMD's GPU division (formerly ATI).

AMD's numbering is similar: RX 7800 XT means 7th generation (7000 series), 800 = high-end, XT = faster version.

Intel Arc Naming

Intel's GPUs are called Arc. The first generation was Alchemist (A-series: A310, A380, A580, A750, A770). The second generation in 2026 is Battlemage (B-series). Intel is the newest player and their cards offer good value for budget builds.

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VRAM – The GPU's Memory (Very Important!)

VRAM stands for Video RAM. It is the memory on your graphics card. Think of it as the GPU's desk — the space where it keeps all the data it is currently working with.

When you play a game, the textures (wall details, character models, environment) are loaded into VRAM. When you edit a video, the frames are stored in VRAM. When you run an AI model, the model itself sits in VRAM.

If VRAM fills up, the GPU has to use your computer's regular RAM (system RAM) as a backup — and that is very slow. You will notice stuttering, lag, and crashes.

How Much VRAM Do You Need?

Usage	Minimum	Recommended	Notes
Office / Browsing	0 (integrated)	0 (integrated)	You don't need a separate GPU at all
Casual Gaming (1080p)	4GB	6GB-8GB	Games like Valorant, CS2, GTA V
Modern Gaming (1080p High)	6GB	8GB-12GB	Cyberpunk, COD, God of War
1440p Gaming	8GB	12GB-16GB	Higher resolution needs more VRAM
4K Gaming	12GB	16GB-24GB	4K textures are huge
Video Editing (1080p)	4GB	8GB	Premiere Pro, DaVinci Resolve
Video Editing (4K)	8GB	12GB-16GB	4K video + effects needs lots of VRAM
3D Modeling / Rendering	8GB	12GB-24GB	Blender, 3ds Max, Maya
AI / Machine Learning	8GB	12GB-24GB+	Running local LLMs needs 12GB+ easily
Data Analysis (Large Datasets)	4GB	8GB-16GB	Pandas, RAPIDS, large Excel files

GPUs for Different Types of Work

Video Editing

Video editing software like Premiere Pro, DaVinci Resolve, and Final Cut Pro use the GPU heavily. Effects, color grading, transitions, and rendering all happen on the GPU. A good GPU can make export times 5-10x faster than relying on the CPU alone.

What to look for: NVIDIA (better Adobe support), at least 8GB VRAM for 1080p, 12GB+ for 4K. Tensor cores help with AI features like scene detection and auto-reframe.

3D Modeling & Rendering

Software like Blender, 3ds Max, Maya, and SketchUp use the GPU for viewport performance (how smooth the 3D scene moves when you rotate it) and for final rendering.

What to look for: More CUDA cores = faster viewport. More VRAM = ability to handle complex scenes with lots of polygons and textures. 12GB+ VRAM is recommended for professional 3D work.

AI & Machine Learning

This is where NVIDIA dominates completely. TensorFlow, PyTorch, Stable Diffusion, and LLaMA all require NVIDIA CUDA. AMD cards work with some workarounds but it is painful.

What to look for: NVIDIA only. Maximum VRAM — this is the single most important spec for AI. A used RTX 3090 with 24GB VRAM can be better for AI than a brand new RTX 4070 with 12GB. For serious AI work, look for 16GB+ VRAM.

Data Analysis & Large Datasets

Tools like RAPIDS (GPU-accelerated pandas), cuDF, and cuML allow data scientists to process huge datasets on GPUs. A GPU can process data 10-50x faster than a CPU for certain operations.

What to look for: Any GPU with sufficient VRAM to hold your dataset in memory. 8GB minimum, 16GB+ recommended for serious data work.

Gaming

For gaming, the GPU is the most important component. The CPU matters too, but the GPU does 80% of the work in games.

What to look for: Any brand works. Look at benchmarks for the specific games you play. Generally, a ₹20,000-₹30,000 GPU (like RTX 4060 or RX 7600) is enough for smooth 1080p gaming. For 1440p, look at ₹35,000-₹50,000 range.

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The Bottom Line

Here is a simple summary to remember:

• CUDA cores = general workers (more = faster, but only compare within NVIDIA)
• Tensor cores = AI specialists (NVIDIA only, used for DLSS and AI apps)
• RT cores = light simulators (for Ray Tracing in games)
• VRAM = the GPU's desk space (more = can handle bigger tasks)
• RTX = NVIDIA with AI & Ray Tracing (modern)
• GTX = NVIDIA without AI & Ray Tracing (older)
• RX = AMD's brand
• Arc = Intel's brand

Continue to Part 3: AMD vs NVIDIA vs Intel Arc — Which One to Buy →