NVIDIA Tesla T10 Graphics Card 16 GB

The Tesla T10 16 GB is a professional graphics card by NVIDIA, launched in 2020. Built on the 12 nm process, and based on the TU102 graphics processor, in its TU102-890-KCD-A1 variant, 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. Unlike the fully unlocked TITAN RTX, which uses the same GPU but has all 4608 shaders enabled, NVIDIA has disabled some shading units on the Tesla T10 16 GB to reach the product’s target shader count. It features 3072 shading units, 192 texture mapping units, and 96 ROPs. Also included are 384 tensor cores which help improve the speed of machine learning applications. The card also has 48 raytracing acceleration cores. NVIDIA has paired 16 GB GDDR6 memory with the Tesla T10 16 GB, which are connected using a 256-bit memory interface. The GPU is operating at a frequency of 1065 MHz, which can be boosted up to 1395 MHz, memory is running at 1575 MHz (12.6 Gbps effective).

Being a single-slot card, the NVIDIA Tesla T10 16 GB draws power from 1x 8-pin power connector, with power draw rated at 150 W maximum. This device has no display connectivity, as it is not designed to have monitors connected to it. Tesla T10 16 GB is connected to the rest of the system using a PCI-Express 3.0 x16 interface. The card measures 267 mm in length, 111 mm in width, and features a single-slot cooling solution.

Key Features:

1. Parallel Processing Architecture

• • High GPU Core Count
    Tesla-class GPUs contain thousands of CUDA cores organized for massive parallelism. This architecture accelerates workloads in areas like scientific computing, AI training, and big data processing—offering speed-ups over traditional CPU-only setups.
  • Efficient Instruction Scheduling
    The T10’s design (likely based on a historical Tesla architecture such as Fermi, Kepler, or later) optimizes thread scheduling, ensuring maximum utilization of GPU cores for sustained throughput and minimized idle cycles.

2. 16GB On-Board Memory

• • High-Capacity GDDR or HBM
    Equipped with 16GB of GPU memory, large data sets, deep learning models, or complex HPC workloads can be loaded in the GPU for reduced host-device transfers.
  • High Memory Bandwidth
    Tesla-series boards typically feature fast memory (e.g., GDDR5, GDDR5X, or HBM depending on the generation), delivering robust bandwidth crucial for large-scale computations and rapid data exchange between GPU cores and memory.

3. Compute-Focused Design

• • ECC Support (Error-Correcting Code)
    Enterprise-grade error-correcting memory capabilities help maintain data integrity in mission-critical tasks or around-the-clock HPC use.
  • Double Precision or Mixed Precision
    Tesla GPUs generally offer strong double-precision floating-point performance, vital for scientific simulations. Modern Tesla variants also implement half or mixed-precision modes for faster AI inference or training operations.

4. Scalable Multi-GPU Configuration

• • NVLink or PCIe Connectivity
    Depending on the generation, the T10 16GB board may connect via PCI Express or NVLink technology, enabling high-speed inter-GPU communication for multi-GPU cluster setups.
  • Optimized for Server Enclosures
    Tesla boards are typically built in a passively cooled form factor for rack-mounted servers, relying on the server’s airflow and power design to manage thermal output.

5. Data Center Reliability

• • Long Lifecycle & Driver Support
    NVIDIA’s Tesla GPUs include extended driver support and reliability features, making them suitable for HPC clusters, research institutions, or enterprise data centers with stable update cycles.
  • Thermal & Power Management
    Robust power management and consistent thermal design ensure stable GPU performance even under continuous heavy loads.

6. Software Ecosystem & Tools

• • CUDA Programming Model
    Programmers can tap into the extensive CUDA toolkit, libraries (cuBLAS, cuFFT, cuDNN, etc.), and frameworks (TensorFlow, PyTorch) that leverage NVIDIA GPUs for parallel computing tasks.
  • NVIDIA HPC & AI Stack
    The Tesla T10 can integrate into the broader NVIDIA HPC/AI software stack, including HPC compilers, containers (NGC), and cluster management solutions.

Applications:

1. High-Performance Computing (HPC)
   • Scientific Simulations: Accelerate weather modeling, computational fluid dynamics (CFD), molecular dynamics, and other HPC workloads.
   • Data Analytics: Process large data sets in parallel, improving throughput for big data pipelines in finance, genomics, or business intelligence.
2. AI & Deep Learning
   • Model Training & Inference: Speed up neural network operations, from natural language processing (NLP) tasks to computer vision classification.
   • Mixed-Precision Computing: Exploit lower-precision math capabilities (FP16/FP32) for faster training cycles or real-time inference in data centers.
3. Virtual Desktop Infrastructure (VDI)
   • Professional Visualization: Some Tesla GPUs can handle remote rendering for professional CAD/CAM, media creation, or engineering applications.
   • Multi-User GPU: Virtual machines can share GPU resources, enabling multiple simultaneous users in a cloud-based environment.
4. Research & Development
   • Experimental Computation: Universities, government labs, and private R&D centers utilize Tesla boards for cutting-edge experiments, algorithm prototyping, and HPC-based discoveries.
   • Machine Learning Exploration: Data scientists rely on GPU-accelerated frameworks for rapid prototyping of novel AI models.
5. Enterprise Clusters & Cloud Services
   • Data Center Acceleration: Deploy Tesla boards in cluster environments or cloud platforms to offer HPC or AI services to end-users.
   • Scalable GPU Farms: Combine multiple Tesla GPUs for large-scale parallel computing tasks, ensuring high availability and performance for mission-critical workloads.

Why Choose the NVIDIA Tesla T10 16GB?

1. Robust GPU Acceleration
   • Delivers significantly higher throughput than CPU-only solutions for parallelizable workloads, boosting performance for HPC, AI, and data analytics tasks.
2. Enterprise Reliability & Support
   • Tesla-series cards come with extended driver support, tested firmware stability, and proven success in HPC/data center deployments worldwide.
3. High Memory Capacity
   • 16GB onboard memory is ideal for moderate to large data sets, enabling deep learning models or HPC computations without frequent transfers between host and device memory.
4. Enhanced Precision & ECC
   • Double-precision floating-point support and error-correcting memory (ECC) maintain accuracy and reliability for scientific or financial calculations.
5. Integration with CUDA & AI Frameworks
   • Leverage an extensive ecosystem of CUDA libraries, HPC/AI frameworks, and developer tools that maximize GPU acceleration capabilities.
6. Flexible Data Center Deployment
   • Available in passively cooled or server-friendly designs, the T10 16GB can scale across multiple nodes in HPC clusters or AI farms.