"The Evolution of NVIDIA Chips: A Technical Analysis of the Last Three Versions"

Author: Wolfshadow Dev

NVIDIA has made giant strides in creating chips for GPUs, raising the standard for both the gaming industry and artificial intelligence and computing in general. Here, we compare the three most recent architectures: Ampere (RTX 30 series), Ada Lovelace (RTX 40 series), and the revolutionary Hopper (H100 series), evaluating their key improvements in power, efficiency, and processing capacity.

Ampere (RTX 30 Series): The Leap to AI and Advanced Ray Tracing

Released in September 2020, the RTX 30 series with Ampere architecture was a significant shift in performance. The use of CUDA cores, RT (Ray Tracing) cores, and Tensor cores allowed Ampere to offer a balance between traditional graphic processing and artificial intelligence capabilities.

Manufacturing Process: 8nm (Samsung).

CUDA Cores: Between 3584 (RTX 3060) and 10496 (RTX 3090).

Ray Tracing Improvement: Second-generation RT cores, which offered double the performance compared to the Turing series.

Tensor Cores: Third-generation Tensor cores, specialized in AI, DLSS (Deep Learning Super Sampling), and compute acceleration.

Memory: Up to 24 GB of GDDR6X, ideal for 8K gaming and AI tasks.

Power: Up to 350W (RTX 3090), with a notable increase in energy consumption, requiring robust power supplies.

Ada Lovelace (RTX 40 Series): The Efficiency and Power of the Third Generation

The RTX 40 series, based on the Ada Lovelace architecture, launched in 2022, was built with the goal of improving energy efficiency and delivering superior performance in both gaming and AI applications.

Manufacturing Process: 4nm (TSMC).

CUDA Cores: From 5888 (RTX 4060) to 16384 (RTX 4090), a significant boost in raw power.

Ray Tracing: New third-generation RT cores with better performance and a remarkable improvement in real-time ray tracing.

Tensor Cores: Fourth-generation, further optimized for deep learning tasks and neural network inference.

DLSS 3.0: Ada Lovelace brought a key update with DLSS 3.0, which not only enhances image quality but also boosts FPS (Frames per Second) by generating additional frames through AI.

Memory: Up to 24 GB of GDDR6X (on the RTX 4090), with a faster memory bus than Ampere, allowing better handling of large data volumes.

Power: Similar to the Ampere series, with a maximum consumption of up to 450W (RTX 4090), maintaining a focus on cooling.

Hopper (H100 Series): The Giant of Artificial Intelligence

The Hopper architecture, launched in 2022 and primarily aimed at data centers and artificial intelligence applications, is a dramatic evolution of previous architectures. It focuses on accelerating AI models at unprecedented speeds.

Manufacturing Process: 4nm (TSMC).

Fourth-generation Tensor Cores: Specifically designed to handle the complex mathematical operations required by AI, such as deep neural network training.

Third-generation NVLink: With interconnects up to 900 GB/s, improving communication between multiple GPUs in data centers.

HBM3: High Bandwidth Memory (HBM3) allows up to 3 TB/s of memory bandwidth, which is critical for massive AI and HPC (High-Performance Computing) workloads.

SM (Streaming Multiprocessors): Increase efficiency in massive parallel processing tasks.

Applications: Ideal for large-scale language models (like GPT) and supercomputing tasks that require high compute density.

Conclusion

NVIDIA's progress with Ampere, Ada Lovelace, and Hopper shows a clear direction toward high-performance computing and artificial intelligence. While Ampere represented a significant leap in improving ray tracing and deep learning, Ada Lovelace focused on improving energy efficiency and processing capability in gaming. Hopper, meanwhile, is a beast designed to take AI and massive computation to a new level. Each generation has refined energy efficiency and improved AI capabilities, with Hopper redefining the standard for artificial intelligence computing.