NVIDIA Tesla T40 Graphics Card 24 GB

The Tesla T40 24 GB is a professional graphics card by NVIDIA. Built on the 12 nm process, and based on the TU102 graphics processor, the card supports DirectX 12 Ultimate. The TU102 graphics processor is a large chip with a die area of 754 mm² and 18,600 million transistors. It features 4608 shading units, 288 texture mapping units, and 96 ROPs. Also included are 576 tensor cores which help improve the speed of machine learning applications. The card also has 72 raytracing acceleration cores. NVIDIA has paired 24 GB GDDR6 memory with the Tesla T40 24 GB, which are connected using a 384-bit memory interface. The GPU is operating at a frequency of 1305 MHz, which can be boosted up to 1560 MHz, memory is running at 1625 MHz (13 Gbps effective).

Being a dual-slot card, the NVIDIA Tesla T40 24 GB draws power from 1x 6-pin + 1x 8-pin power connector, with power draw rated at 260 W maximum. This device has no display connectivity, as it is not designed to have monitors connected to it. Tesla T40 24 GB is connected to the rest of the system using a PCI-Express 3.0 x16 interface. The card measures 267 mm in length, and features a dual-slot cooling solution.

Key Features:

1. Compute-Oriented Architecture

• High CUDA Core Count
  • The T40 is expected to include thousands of CUDA Cores, enabling massive parallel processing essential for scientific computing, deep learning training, and large-scale data analytics.
• Tensor Cores
  • Designed to speed up AI and machine learning tasks, Tensor Cores deliver faster training and inference for neural network operations in mixed-precision math (FP16, BF16, etc.).
• Ray Tracing Acceleration (If Turing/Ampere-Based)
  • Depending on the generation, second- or third-generation RT Cores could facilitate hardware-accelerated ray tracing for advanced visualization or design rendering (though HPC GPUs often focus less on this feature).

2. 24 GB of High-Speed Memory

• Ample VRAM Capacity
  • The T40’s 24 GB of GDDR6 (or possibly HBM2/HBM2e depending on design) ensures it can handle massive models, multi-terabyte datasets (in distributed scenarios), and multiple high-resolution data streams.
• ECC Support
  • Error-correcting code memory is crucial for HPC and AI operations where precision and data integrity are paramount, minimizing system errors during lengthy computations.

3. HPC & AI Focus

• Mixed-Precision Computing
  • Tensor Cores allow for half-precision (FP16), BF16, or even INT8 operations, significantly accelerating deep learning training and inference while maintaining acceptable model accuracy.
• Reliable 24/7 Operation
  • Engineered for sustained performance under continuous heavy load, enabling HPC labs, data center AI clusters, or large-scale analytics tasks to run uninterrupted.

4. Energy Efficiency and Data Center Integration

• Optimized Thermal Design
  • Often available in passively cooled or specialized form factors, suitable for server chassis with robust airflow.
• Potential Multi-GPU Scaling
  • HPC-grade GPUs frequently support bridging (e.g., NVLink or custom HPC interconnect) to unify memory or increase compute capacity for exascale or multi-petaflop systems.

5. Enterprise Software Support

• Long-Life Drivers & Security Patches
  • Enterprise-tier driver branches and extended support windows ensure stable performance, security updates, and minimal downtime in mission-critical HPC or AI.
• NVIDIA HPC/AI Ecosystem
  • Compatible with NVIDIA’s HPC libraries, CUDA-X AI, cuDNN, and RAPIDS for data analytics, simplifying environment configuration and job orchestration in HPC clusters or cloud platforms.

Applications:

1. High-Performance Computing (HPC)

1. • Scientific Simulations
     – Accelerates climate modeling, molecular dynamics, astrophysics, CFD, and other computationally heavy tasks.
   • Engineering & Research
     – Reduces iteration times for structural analysis, finite element modeling, and advanced research prototypes.

2. AI & Deep Learning

1. • Large-Scale Training
     – Tensor Cores speed up backpropagation and matrix operations, enabling faster training for large neural networks like transformers or convolutional models.
   • Distributed Inference
     – Provides GPU-accelerated predictions in data centers handling thousands of queries or real-time streaming analytics.

3. Data Analytics & Big Data

1. • GPU-Accelerated SQL
     – Tools like RAPIDS or BlazingSQL can harness GPU parallelism, making big data queries more efficient and interactive.
   • ETL & Real-Time Processing
     – Simplifies data pipelines by offloading transformations, merges, and aggregations to the GPU, offering quick insights.

4. Professional Visualization

1. • Ray-Traced Rendering
     – If Turing/Ampere-based, advanced ray tracing can deliver near-real-time rendering for 3D design, VFX, or virtual prototyping.
   • Immersive VR
     – Potentially handles complex VR simulations in engineering, healthcare, or design training scenarios.

5. Enterprise & Cloud Deployments

1. • Virtualization (vGPU)
     – HPC or AI tasks can be assigned to GPU partitions, or GPU resources shared among multiple workloads or user sessions.
   • Hybrid Cloud
     – Integrates with containerized HPC/AI services, bridging on-prem and cloud GPU clusters seamlessly.

Why Choose the NVIDIA Tesla T40 Graphics Card 24 GB?

1. HPC & AI Performance

1. • Combines a large core count, ample memory, and advanced features (Tensor/RT Cores) for top-tier HPC computations, deep learning, and data analytics.

2. 24 GB Memory for Large-Scale Tasks

1. • Offers substantial VRAM to accommodate complex 3D scenes, expansive datasets, or advanced AI models without frequent out-of-memory issues.

3. Data Center-Ready

1. • Built for uninterrupted 24/7 usage, robust enterprise driver support, and integration with HPC/AI ecosystems, ensuring reliability and minimal downtime.

4. Flexible Multi-GPU Scaling

1. • HPC environments can combine multiple T40 boards (or pair them with other HPC solutions) for near-linear scale in computational tasks or memory pooling (if interconnect is provided).

5. Future-Proofing

1. • Ampere-based architecture (if Turing/Ampere) ensures support for the latest HPC libraries, AI frameworks, and real-time ray tracing developments.