900-2G133-0000-000 Nvidia Ampere A40 48GB GDDR6 2 Slot PCI-E Gen 4.0 GPU

Nvidia Ampere A40 48GB GDDR6 GPU Overview

The Nvidia 900-2G133-0000-000 Ampere A40 48GB GDDR6 with ECC 2 Slot PCI-Express Gen 4.0 NVLink Multi-Workload Graphics Processing Card category represents a class of professional, data-center-grade GPUs engineered for diverse, high-demand compute and visualization environments. This category targets enterprises, research institutions, studios, and cloud service providers that require a robust balance of large memory capacity, error-correcting memory reliability, PCIe Gen4 throughput, and a compact two-slot form factor that fits dense server and workstation deployments. The Ampere A40 family delivers a flexible multi-workload platform that consolidates AI training and inference, GPU-accelerated compute, high-fidelity rendering, complex simulation, and virtual desktop infrastructure (VDI) into a single GPU platform, enabling organizations to simplify procurement, reduce hardware sprawl, and improve utilization across workloads.

Designed

Workloads in modern compute infrastructures are diverse and often need to coexist on the same hardware. The Ampere A40 category is positioned to satisfy multi-workload requirements by providing a combination of large 48GB GDDR6 with ECC memory and PCIe Gen4 connectivity that supports high data throughput from host CPUs and storage. This makes the A40 ideal for environments where machine learning pipelines must run alongside rendering jobs and visualization sessions without sacrificing reliability or memory capacity. The inclusion of ECC memory ensures enhanced data integrity during long-running compute tasks, an important consideration in mission-critical simulations, financial modeling, and scientific research where bit errors can invalidate results. Organizations that consolidate workloads on Ampere A40 cards can reduce the need for separate, specialized hardware, lowering total cost of ownership while maintaining predictable performance for heterogeneous tasks.

Memory

Memory capacity directly impacts the size of models, datasets, and scenes that can be processed without costly offloading or partitioning. With 48GB of GDDR6 memory, the Ampere A40 category supports large neural networks, high-resolution datasets, and complex 3D scenes used in professional visualization workflows. ECC (Error-Correcting Code) memory adds an essential layer of protection that detects and corrects single-bit memory errors, making the A40 suitable for production systems where accuracy and repeatability matter. This combination of plentiful memory and ECC reliability positions the category as a preferred choice for users who need to push the boundaries of model size or dataset fidelity while maintaining data correctness over extended runs.

PCI-Express Gen 4.0

The 2-slot PCI-Express Gen 4.0 interface in this category provides a high-bandwidth channel between the GPU and host system, enabling faster movement of data for workloads that require frequent host–device transfers. PCIe Gen4 doubles the per-lane bandwidth of Gen3, improving throughput for data-intensive tasks such as pre-processing, streaming large datasets, and moving intermediate results. The Gen4 interface simplifies system design choices by offering more headroom for future workloads that demand higher host connectivity. For system integrators and data center architects, PCIe Gen4 compatibility means that Ampere A40 cards can be leveraged in newer server platforms to maximize end-to-end data flow, improving overall job completion times and system efficiency.

Form Factor

The two-slot form factor of this category strikes an effective balance between thermal design and space efficiency. Two-slot GPUs are easier to fit into standard server and workstation chassis compared to larger multi-slot accelerators, allowing denser GPU deployments in rack servers and smaller professional workstations. This compactness is particularly advantageous for organizations that run large numbers of GPU-enabled nodes, as it increases the number of accelerators per rack unit while keeping cooling and power requirements manageable. The Ampere A40 category’s physical profile simplifies upgrades in existing data center racks and workstation builds where space and airflow are constraints.

NVLink and Multi-GPU Scaling

NVLink connectivity is an important aspect of this category for users who need to scale across multiple GPUs while maintaining high-speed, low-latency communication. NVLink allows GPUs to share memory coherently and transfer data much faster than traditional interconnects, enabling larger effective memory pools and more efficient parallelization for multi-GPU training or rendering tasks. When used in supported configurations, NVLink enables the Ampere A40 to act as a member of a tightly-coupled GPU cluster that accelerates distributed machine learning, large-scale simulations, and multi-GPU visual compute. For enterprises running cluster-level workloads, NVLink-capable cards reduce the overhead of data sharding and synchronization, leading to better scalability and higher GPU utilization.

Use Cases

The Ampere A40 category is well-suited to a wide range of compute-intensive use cases. In artificial intelligence and data science, the card provides the memory footprint needed for high-capacity models, feature-rich datasets, and real-time inference at scale. Data scientists benefit from the ability to prototype larger models locally and run end-to-end pipelines without memory-induced bottlenecks. In high-performance computing and simulation workloads, the A40 can accelerate numerical methods, finite-element analysis, computational fluid dynamics, and other simulation tasks that are sensitive to memory bandwidth and capacity. The card’s architecture and multi-workload orientation also make it a practical choice for mixed-load data centers that must pivot between training, inference, and visualization responsibilities quickly and efficiently.

Performance

Extracting maximum performance from the Ampere A40 category requires attention to the entire compute stack. Memory-bound workloads must be tuned to use the 48GB of GDDR6 effectively by optimizing batch sizes, data prefetching, and memory layout. PCIe Gen4 benefits are realized when host systems also support Gen4 lanes, reducing transfer latency and improving throughput. NVLink configurations should be used when multi-GPU memory sharing or high-bandwidth inter-GPU transfers are critical. For compute kernels and rendering engines, developers can use optimized libraries and frameworks that exploit Ampere architecture features, accelerating compute kernels and minimizing overhead. The category’s flexibility makes it suitable for iterative tuning — balancing model architecture, dataset partitioning, and job scheduling to maximize utilization and throughput across heterogeneous workloads.

Comparisons

Within the broader Nvidia product family, the Ampere A40 category occupies a unique position aimed at professional, data-center, and workstation customers who require a combination of large memory, ECC reliability, and compact form factor. Compared to consumer gaming GPUs, the A40 emphasizes error correction, management features, and driver stability tailored to professional applications rather than consumer-oriented gaming performance. Versus larger, purpose-built accelerators designed exclusively for massive-scale AI training, the A40 balances capability with density, making it easier to deploy across a broader set of use cases. This middle ground is particularly valuable for organizations that need to run varied workloads and cannot dedicate separate systems for each specialized task.

Integration

Effective integration demands awareness of power, cooling, and system compatibility. The Ampere A40 category’s two-slot design helps with density planning, but operators must still carefully validate power delivery and thermal solutions to prevent throttling under sustained load. Rack-level planning should consider airflow, fan speed profiles, and thermal zones to ensure predictable performance. Coordinating firmware, BIOS settings, and driver versions across compute nodes reduces variability and eases debugging. Finally, selecting storage and network architectures that complement the A40’s data-handling capabilities will ensure that the GPU is fed with data at rates that avoid bottlenecks, unlocking the full value of the hardware investment.