Introduction
Graphics Processing Units (GPUs) were originally designed to handle computer graphics, like making video games look realistic or helping Netflix stream smoothly to your TV. If you've ever played a video game, watched a high-quality video, or edited photos, you've already benefited from a GPU.
Think of your computer like a kitchen. The CPU (Central Processing Unit) is like a chef who follows a recipe one step at a time — great for general tasks. The GPU, on the other hand, is like a team of sous chefs who can chop, stir, and prep multiple ingredients at once. That ability to do many tasks in parallel is what makes GPUs special.
Why do we need GPUs for AI
AI, especially machine learning, involves a lot of math. It needs to analyze massive amounts of data and perform billions of calculations quickly. This is where GPUs come in—they can do thousands of calculations at the same time, making AI models train much faster.
Imagine you're trying to solve a huge puzzle. If you're working alone (like a CPU), it might take hours or days. But if you have a whole team working together (like a GPU), the puzzle comes together much faster. This is why AI researchers rely on GPUs to train models efficiently.
How Do GPUs Contribute to AI?
GPUs are the engines behind many of the AI applications you use every day, including:
- Chatbots and Virtual Assistants: AI models like ChatGPT process huge amounts of text data, and GPUs help them respond quickly and intelligently.
- Image Recognition: Whether it's unlocking your phone with Face ID or helping doctors detect diseases in medical scans, GPUs enable AI to recognize and process images at lightning speed.
- Self-Driving Cars: AI-powered vehicles analyze their surroundings in real-time, making decisions in milliseconds. GPUs process all that data to keep them on the road safely.
- Recommendation Systems: Ever wonder how Netflix knows what movie you might like? Or how Amazon suggests the perfect product? AI models trained on GPUs analyze your preferences and predict what you'll enjoy.
GPU Glossary
Before diving into the technical details, here's a glossary that will help you navigate this guide.
|
Term |
Definition |
|
Cores |
The fundamental processing units within a GPU. Think of cores as individual calculators that can perform mathematical operations. Modern GPUs contain thousands of these cores working in parallel, which is what gives them their computational power for AI workloads. |
|
CUDA Cores |
NVIDIA's general-purpose parallel processing cores that handle the bulk of a wide variety of computational work. |
|
NVIDIA Grace CPU |
NVIDIA's Arm-based CPU designed specifically for AI, data analytics, and high-performance computing workloads. NVIDIA Grace CPUs are optimized to work seamlessly with NVIDIA GPUs, providing top performance and energy efficiency AI applications. |
|
Tensor Cores |
Specialized cores designed specifically for the matrix operations that dominate AI training and inference. |
|
RT Cores |
Cores built for accelerating real-time ray tracing in graphics, these cores excel at simulating the physical behavior of light and accelerating applications involving 3D assets. |
|
Memory Bandwidth |
The rate at which data can be read from or written to memory, measured in TB/s (terabytes per second). This becomes crucial for inference workloads that are memory-bound. |
|
Precision Formats (FP32, FP16, FP8, FP4, etc.) |
Different ways of representing numbers in computer memory, with lower precision formats using less memory and computation but potentially sacrificing some numerical accuracy. |
|
High Bandwidth Memory (HBM) |
Advanced memory technology that stacks memory chips vertically to achieve extremely high bandwidth. HBM3e is the latest evolution with HBM4 coming soon, providing even faster data transfer rates essential for feeding thousands of GPU cores simultaneously. |
|
Peripheral Component Interconnect Express (PCIe) |
The standard interface that connects GPUs to the rest of the computer system. Higher PCIe versions (like PCIe 5.0) provide faster data transfer between the GPU and CPU/system memory. |
|
NVIDIA NVLink and NVLink Switch |
NVIDIA's fifth generation NVLink high-speed interconnect technology allows direct GPU-to-GPU communication at 14x higher bandwidth than PCIe Gen 5, enabling efficient multi-GPU setups for scale-up AI training and inference. |
|
Total Graphics Power (TGP) |
The maximum power consumption of the GPU under full load, measured in watts. This determines cooling requirements and power infrastructure needs for data centers. |
|
Graphics Double Data Rate 6 (GDDR6) |
A type of high-speed memory used in graphics cards that offers faster data transfer rates than standard system RAM while being more cost-effective than High Bandwidth Memory (HBM). |
|
Error-Correcting Code (ECC) |
Memory technology that can detect and correct data corruption, crucial for production AI systems where data integrity is essential. ECC memory is typically found in professional/datacenter GPUs. |
|
Floating Point Operations Per Second (FLOPS ) |
A measure of computational performance indicating how many mathematical calculations a processor can perform per second. Modern GPUs achieve petaFLOPS (quadrillions of operations per second), with higher FLOPS meaning faster AI training and inference. |
Lambda Offerings
Lambda GPU Breakdown
NVIDIA H100 GPU
This is built on NVIDIA’s Hopper architecture and delivers great performance for large-scale AI training and inference
Specifications:
- 3,958 teraFLOPS of FP8 with sparsity
- 80GB HBM3 memory with 3TB/s memory bandwidth
- Support for FP8, FP16, BF16, TF32, FP64 and INT8 precision formats
- 700W TGP
- PCIe 5.0 and Fourth-Generation NVIDIA NVLink connectivity
AI-Specific Features:
- Transformer Engine with FP8 precision support
- Multi-Instance GPU technology allowing up to 7 isolated GPU instances
The NVIDIA H100 GPU FP8 support is particularly significant for modern AI workloads. This format reduces memory usage while maintaining accuracy, making it ideal for training and deploying large language models where memory can become the limiting factor.
NVIDIA H200 GPU
The NVIDIA H200 GPU builds upon the NVIDIA H100 architecture with crucial memory improvements:
Key Upgrades:
- 141GB HBM3e memory (76% increase over H100)
- 4.8TB/s memory bandwidth (60% increase)
- Same compute architecture as H100 but with enhanced memory subsystem
This expanded memory capacity is particularly valuable for inference workloads with large language models, where entire models can fit in a single GPU for optimal performance. The H200 can handle models with significantly more parameters or support larger batch sizes for inference serving.
NVIDIA Blackwell GPUs represent a significant architectural leap:
Specifications:
- 208 billion transistors (2.5x more than H100)
- Up to 192GB HBM3e memory with 8TB/s bandwidth
- Second-generation Transformer Engine with FP4 precision support
- Up to 20 petaFLOPS of FP4 compute performance
- 1000W TGP with advanced cooling requirements
FP4 Precision: The introduction of FP4 (4-bit floating point) represents a breakthrough in AI efficiency. While maintaining acceptable numerical accuracy for many AI workloads, FP4 can theoretically provide more throughput and dramatically reduce memory requirements for model weights.
Grace Blackwell Integration: The Blackwell GPU can be paired with NVIDIA's Grace CPUs in a unified architecture called GB200 Grace Blackwell Superchip, providing coherent memory access between CPU and GPU through NVIDIA NVLink-C2C high-speed chip-to-chip interconnect. This is particularly valuable for workloads that require significant CPU pre/post-processing alongside GPU compute.
Which GPU Should You Choose?
Next-Generation Hardware: What's Coming to Lambda GPU-Cloud
Based on NVIDIA's official announcements at NVIDIA GTC 2025, here's preliminary information for what's on the horizon with Lambda's infrastructure:
NVIDIA Blackwell Ultra (NVIDIA GB300 NVL72): Built for the Age of AI Reasoning
Second Half 2025
The Blackwell Ultra represents a mid-cycle refresh with significant upgrades:
- Enhanced Memory Capacity: 288GB HBM3E per GPU (up from 192GB)
- Improved Performance: 1.5x more compute than GB200 for AI inference
- Upgraded Connectivity: ConnectX-8 SuperNIC networking, doubling aggregate bandwidth to 14.4 TB/s
- Same Architecture: Built on proven Blackwell architecture with optimizations
NVIDIA Vera Rubin (Vera Rubin NVL144)
Second Half 2026
NVIDIA's next major architectural leap features:
- Computing Power: 3.6 EFLOPS FP4 per NVL144 rack (50 PFLOPS inference per GPU)
- Memory: 75TB of fast memory per NVL144 rack (288GB HBM4 per GPU with improved bandwidth)
- CPU Architecture: 88 custom ARM cores with 176 threads per Vera CPU
- Faster Interconnect: 6th generation NVLink with 260 TB/s aggregate switching
- Improved Networking: ConnectX-9 SuperNICs with 28.8 TB/s aggregate bandwidth
Preliminary specs. Subject to change.
Rubin Ultra (Vera Rubin Ultra NVL576)
Second Half 2027
Features "Kyber" rack design:
- Massive Compute Density: 576 Rubin GPU die per rack (vs. 144 in VR200)
- Extreme Performance: 15 EFLOPS FP4 inference (100 PFLOPS per packaged GPU)
- Enhanced Memory: 1TB HBM4e per GPU with improved bandwidth
- Advanced Interconnect: 7th generation NVLink with 1.5 PB/s aggregate switching
Preliminary specs. Subject to change.
The Future of AI and GPUs
As AI continues to evolve, GPUs and rack-scale architectures will remain a crucial part of the equation. Scientists are constantly developing newer, more powerful GPU systems that can handle even bigger AI challenges — like simulating human brain activity, generating realistic images and videos, and advancing scientific research.