Home / GPU Comparison / NVIDIA B300 or NVIDIA A800 PCIe 80 GB: What's better?

NVIDIA B300

vs

NVIDIA A800 PCIe 80 GB

GPU Comparison Result

Below are the results of a comparison of NVIDIA B300 and NVIDIA A800 PCIe 80 GB video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

NVIDIA B300

Higher Boost Clock: 2600 MHz (2600 MHz vs 1410MHz)
Larger Memory Size: 144GB (144GB vs 80GB)
More Shading Units: 20480 (20480 vs 6912)
Newer Launch Date: September 2025 (September 2025 vs November 2022)

NVIDIA A800 PCIe 80 GB

Higher Bandwidth: 2039 GB/s (4.10TB/s vs 2039 GB/s)

Basic

NVIDIA

Label Name

NVIDIA

September 2025

Launch Date

November 2022

Desktop

Platform

Professional

B300

Model Name

A800 PCIe 80 GB

Server Blackwell

Generation

Ampere

1665 MHz

Base Clock

1065MHz

2600 MHz

Boost Clock

1410MHz

PCIe 5.0 x16

Bus Interface

PCIe 4.0 x16

104 billion

Transistors

54,200 million

640

Tensor Cores

Tensor Cores are specialized processing units designed specifically for deep learning, providing higher training and inference performance compared to FP32 training. They enable rapid computations in areas such as computer vision, natural language processing, speech recognition, text-to-speech conversion, and personalized recommendations. The two most notable applications of Tensor Cores are DLSS (Deep Learning Super Sampling) and AI Denoiser for noise reduction.

432

640

TMUs

Texture Mapping Units (TMUs) serve as components of the GPU, which are capable of rotating, scaling, and distorting binary images, and then placing them as textures onto any plane of a given 3D model. This process is called texture mapping.

432

TSMC

Foundry

TSMC

5 nm

Process Size

7 nm

Blackwell Ultra

Architecture

Ampere

Memory Specifications

144GB

Memory Size

80GB

HBM3e

Memory Type

HBM2e

4096bit

Memory Bus

The memory bus width refers to the number of bits of data that the video memory can transfer within a single clock cycle. The larger the bus width, the greater the amount of data that can be transmitted instantaneously, making it one of the crucial parameters of video memory. The memory bandwidth is calculated as: Memory Bandwidth = Memory Frequency x Memory Bus Width / 8. Therefore, when the memory frequencies are similar, the memory bus width will determine the size of the memory bandwidth.

5120bit

2000 MHz

Memory Clock

1593MHz

4.10TB/s

Bandwidth

Memory bandwidth refers to the data transfer rate between the graphics chip and the video memory. It is measured in bytes per second, and the formula to calculate it is: memory bandwidth = working frequency × memory bus width / 8 bits.

2039 GB/s

Display and Media

No outputs

Outputs

No outputs

Theoretical Performance

62.40 GPixel/s

Pixel Rate

Pixel fill rate refers to the number of pixels a graphics processing unit (GPU) can render per second, measured in MPixels/s (million pixels per second) or GPixels/s (billion pixels per second). It is the most commonly used metric to evaluate the pixel processing performance of a graphics card.

225.6 GPixel/s

1664.0 GTexel/s

Texture Rate

Texture fill rate refers to the number of texture map elements (texels) that a GPU can map to pixels in a single second.

609.1 GTexel/s

426.0 TFLOPS

FP16 (half)

An important metric for measuring GPU performance is floating-point computing capability. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy.

77.97 TFLOPS

1.664 TFLOPS

FP64 (double)

An important metric for measuring GPU performance is floating-point computing capability. Double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy, while single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.

9.746 TFLOPS

105.525 TFLOPS

FP32 (float)

An important metric for measuring GPU performance is floating-point computing capability. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.

19.88 TFLOPS

Miscellaneous

160

SM Count

Multiple Streaming Processors (SPs), along with other resources, form a Streaming Multiprocessor (SM), which is also referred to as a GPU's major core. These additional resources include components such as warp schedulers, registers, and shared memory. The SM can be considered the heart of the GPU, similar to a CPU core, with registers and shared memory being scarce resources within the SM.

108

20480

Shading Units

The most fundamental processing unit is the Streaming Processor (SP), where specific instructions and tasks are executed. GPUs perform parallel computing, which means multiple SPs work simultaneously to process tasks.

6912

256 KB (per SM)

L1 Cache

192 KB (per SM)

50 MB

L2 Cache

80MB

1400W

TDP

250W

3.0

OpenCL Version

3.0

10.3

CUDA

8.0

Power Connectors

8-pin EPS

ROPs

The Raster Operations Pipeline (ROPs) is primarily responsible for handling lighting and reflection calculations in games, as well as managing effects like anti-aliasing (AA), high resolution, smoke, and fire. The more demanding the anti-aliasing and lighting effects in a game, the higher the performance requirements for the ROPs; otherwise, it may result in a sharp drop in frame rate.

160

1800 W

Suggested PSU

600W