GEFORCE

NVIDIA A100X

NVIDIA graphics card specifications and benchmark scores

80 GB
VRAM
1440
MHz Boost
300W
TDP
5120
Bus Width
🤖Tensor Cores

NVIDIA A100X Specifications

⚙️

A100X GPU Core

Shader units and compute resources

The NVIDIA A100X GPU core specifications define its raw processing power for graphics and compute workloads. Shading units (also called CUDA cores, stream processors, or execution units depending on manufacturer) handle the parallel calculations required for rendering. TMUs (Texture Mapping Units) process texture data, while ROPs (Render Output Units) handle final pixel output. Higher shader counts generally translate to better GPU benchmark performance, especially in demanding games and 3D applications.

Shading Units
6,912
Shaders
6,912
TMUs
432
ROPs
160
SM Count
108
⏱️

A100X Clock Speeds

GPU and memory frequencies

Clock speeds directly impact the A100X's performance in GPU benchmarks and real-world gaming. The base clock represents the minimum guaranteed frequency, while the boost clock indicates peak performance under optimal thermal conditions. Memory clock speed affects texture loading and frame buffer operations. The A100X by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.

Base Clock
795 MHz
Base Clock
795 MHz
Boost Clock
1440 MHz
Boost Clock
1,440 MHz
Memory Clock
1593 MHz 3.2 Gbps effective
GDDR GDDR 6X 6X

NVIDIA's A100X Memory

VRAM capacity and bandwidth

VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The A100X's memory capacity determines how well it handles high-resolution textures and multiple displays. Memory bandwidth, measured in GB/s, affects how quickly data moves between the GPU and VRAM. Higher bandwidth improves performance in memory-intensive scenarios like 4K gaming. The memory bus width and type (GDDR6, GDDR6X, HBM) significantly influence overall GPU benchmark scores.

Memory Size
80 GB
VRAM
81,920 MB
Memory Type
HBM2e
VRAM Type
HBM2e
Memory Bus
5120 bit
Bus Width
5120-bit
Bandwidth
2.04 TB/s
💾

A100X by NVIDIA Cache

On-chip cache hierarchy

On-chip cache provides ultra-fast data access for the A100X, reducing the need to fetch data from slower VRAM. L1 and L2 caches store frequently accessed data close to the compute units. AMD's Infinity Cache (L3) dramatically increases effective bandwidth, improving GPU benchmark performance without requiring wider memory buses. Larger cache sizes help maintain high frame rates in memory-bound scenarios and reduce power consumption by minimizing VRAM accesses.

L1 Cache
192 KB (per SM)
L2 Cache
80 MB
📈

A100X Theoretical Performance

Compute and fill rates

Theoretical performance metrics provide a baseline for comparing the NVIDIA A100X against other graphics cards. FP32 (single-precision) performance, measured in TFLOPS, indicates compute capability for gaming and general GPU workloads. FP64 (double-precision) matters for scientific computing. Pixel and texture fill rates determine how quickly the GPU can render complex scenes. While real-world GPU benchmark results depend on many factors, these specifications help predict relative performance levels.

FP32 (Float)
19.91 TFLOPS
FP64 (Double)
9.953 TFLOPS (1:2)
FP16 (Half)
79.63 TFLOPS (4:1)
Pixel Rate
230.4 GPixel/s
Texture Rate
622.1 GTexel/s

A100X Ray Tracing & AI

Hardware acceleration features

The NVIDIA A100X includes dedicated hardware for ray tracing and AI acceleration. RT cores handle real-time ray tracing calculations for realistic lighting, reflections, and shadows in supported games. Tensor cores (NVIDIA) or XMX cores (Intel) accelerate AI workloads including DLSS, FSR, and XeSS upscaling technologies. These features enable higher visual quality without proportional performance costs, making the A100X capable of delivering both stunning graphics and smooth frame rates in modern titles.

Tensor Cores
432
🏗️

Ampere Architecture & Process

Manufacturing and design details

The NVIDIA A100X is built on NVIDIA's Ampere architecture, which defines how the GPU processes graphics and compute workloads. The manufacturing process node affects power efficiency, thermal characteristics, and maximum clock speeds. Smaller process nodes pack more transistors into the same die area, enabling higher performance per watt. Understanding the architecture helps predict how the A100X will perform in GPU benchmarks compared to previous generations.

Architecture
Ampere
GPU Name
GA100
Process Node
7 nm
Foundry
TSMC
Transistors
54,200 million
Die Size
826 mm²
Density
65.6M / mm²
🔌

NVIDIA's A100X Power & Thermal

TDP and power requirements

Power specifications for the NVIDIA A100X determine PSU requirements and thermal management needs. TDP (Thermal Design Power) indicates the heat output under typical loads, guiding cooler selection. Power connector requirements ensure adequate power delivery for stable operation during demanding GPU benchmarks. The suggested PSU wattage accounts for the entire system, not just the graphics card. Efficient power delivery enables the A100X to maintain boost clocks without throttling.

TDP
300 W
TDP
300W
Power Connectors
1x 16-pin
Suggested PSU
700 W
📐

A100X by NVIDIA Physical & Connectivity

Dimensions and outputs

Physical dimensions of the NVIDIA A100X are critical for case compatibility. Card length, height, and slot width determine whether it fits in your chassis. The PCIe interface version affects bandwidth for communication with the CPU. Display outputs define monitor connectivity options, with modern cards supporting multiple high-resolution displays simultaneously. Verify these specifications against your case and motherboard before purchasing to ensure a proper fit.

Slot Width
Dual-slot
Length
267 mm 10.5 inches
Height
112 mm 4.4 inches
Bus Interface
PCIe 4.0 x8
Display Outputs
No outputs
Display Outputs
No outputs
🎮

NVIDIA API Support

Graphics and compute APIs

API support determines which games and applications can fully utilize the NVIDIA A100X. DirectX 12 Ultimate enables advanced features like ray tracing and variable rate shading. Vulkan provides cross-platform graphics capabilities with low-level hardware access. OpenGL remains important for professional applications and older games. CUDA (NVIDIA) and OpenCL enable GPU compute for video editing, 3D rendering, and scientific applications. Higher API versions unlock newer graphical features in GPU benchmarks and games.

OpenCL
3.0
CUDA
8.0
📦

A100X Product Information

Release and pricing details

The NVIDIA A100X is manufactured by NVIDIA as part of their graphics card lineup. Release date and launch pricing provide context for comparing GPU benchmark results with competing products from the same era. Understanding the product lifecycle helps evaluate whether the A100X by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.

Manufacturer
NVIDIA
Release Date
Jun 2021
Production
End-of-life
Predecessor
Tesla Turing
Successor
Server Ada

A100X Benchmark Scores

📊

No benchmark data available for this GPU.

About NVIDIA A100X

The NVIDIA A100X occupies a unique and premium tier in the data center and AI accelerator market, designed for enterprises and research institutions where performance and memory capacity are non-negotiable. Its 80 GB of HBM2e memory, coupled with a 300W TDP, positions it for massive model training and high-fidelity simulation workloads that choke lesser cards. When evaluating cost, the NVIDIA A100X represents a significant capital investment, justified only by its ability to drastically reduce time-to-solution in commercial AI and scientific computing. This accelerator is not for casual builders or standard server deployments; it targets hyperscale infrastructure and private cloud builds where its total cost of ownership is measured against team productivity and research breakthroughs. Organizations must carefully model their workflow scalability against the substantial upfront price, as the card's value is unlocked at scale. Key considerations for the NVIDIA A100X investment include:

  • Exceptional memory bandwidth for large dataset processing
  • Professional driver and software stack support
  • High power and cooling infrastructure requirements
  • Licensing costs for optimized AI frameworks
  • Integration expenses for compatible server platforms
  • ROI based on computational throughput versus cloud alternatives

Market positioning for the NVIDIA A100X is clear: it serves as a foundational engine for on-premises AI factories and high-performance computing clusters, sitting between the standard A100 and the later-released H100 in NVIDIA's lineage. Built on the Ampere architecture and 7nm process, it offers a balance of mature software ecosystem support and substantial performance headroom for most current generative AI and analytics tasks. While not the absolute latest generation, its feature set, including PCIe 4.0 connectivity, remains highly relevant for systems not yet adopting the newest NVLink-centric platforms. This makes the NVIDIA A100X a strategic purchase for organizations seeking to deploy proven, stable technology without the early-adopter premiums and potential teething issues of newer architectures. It competes directly with other high-memory accelerators and in-house ASIC solutions, winning on the breadth of its CUDA-based software ecosystem. The card's release in mid-2021 places it as a seasoned performer in a rapidly evolving field, still capable of tackling the majority of intensive workloads today.

Future-proofing a system with the NVIDIA A100X involves acknowledging its strengths in memory capacity and its established role in a multi-year product cycle. The 80 GB frame buffer is a critical asset for increasingly large language and multimodal models, providing a measure of longevity against growing model sizes. Its Ampere architecture is fully supported across all major AI and compute frameworks, ensuring software viability for the foreseeable development lifecycle. However, builders must note its PCIe 4.0 x8 interface, which, while fast, may become a bottleneck in future multi-GPU setups compared to full x16 or next-gen PCIe 5.0 slots. Deploying the NVIDIA A100X in a scalable server chassis with robust power delivery and liquid cooling potential is essential to maximize its operational lifespan and performance consistency. Strategic acquisition now can extend the card's utility for several years, especially in mixed-fleet environments where it can handle specific memory-bound tasks.

Build recommendations for the NVIDIA A100X necessitate a professional server-grade approach, as it is not a consumer graphics card. A compatible system requires a server motherboard with adequate PCIe 4.0 lane allocation, a high-wattage redundant power supply, and a chassis engineered for sustained thermal dissipation of 300 watts per card. Enterprise-grade components are mandatory, from ECC system memory to NVMe storage arrays that can feed data fast enough to keep the GPU saturated. The ideal use case for the NVIDIA A100X is within a dedicated compute node as part of a larger cluster, managed by orchestration software like Kubernetes with NVIDIA device plugins. Organizations should partner with certified system integrators or OEMs like Dell, HPE, or Supermicro who offer validated configurations and global support contracts. Ultimately, successfully leveraging the NVIDIA A100X means building not just a PC, but a reliable and scalable computational appliance.

The AMD Equivalent of A100X

Looking for a similar graphics card from AMD? The AMD Radeon RX 6700 offers comparable performance and features in the AMD lineup.

AMD Radeon RX 6700

AMD • 10 GB VRAM

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