Name: 90Y2338 IBM nVidia Tesla K10 8GB GDDR5 Computing Accelerator GK104 Kepler GPUs
Brand: IBM
SKU: 90Y2338
Price: 1471.00 USD
Availability: InStock
Rating: 4.8 (119 reviews)

Product Overview
The IBM nVidia Tesla K10 combines two Kepler-based GPUs on a single accelerator card to tackle demanding compute tasks. It fits well into high-performance computing setups where parallel processing power is required for scientific calculations, simulations, and analytics.

General Information
Brand	IBM
Part Number	90Y2338
Alternate Part Number	90Y2338-03-CT

Technical Information
Chipset	Nvidia
GPU Clock	745 MHz
Suggested PSU	600W

Memory
Memory Size	8GB
Interface	GDDR5

Miscellaneous
Assembly Required	Yes
Eco Friendly	Yes
Compliance Standards	WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL

Physical Characteristics
Slot Width	Dual-slot
Weight	3.00
Condition	Refurbished

Product Description
The IBM nVidia Tesla K10 is a powerful computing accelerator built for handling large-scale parallel workloads. It often finds its place in high-performance computing environments where rapid calculations and efficient processing are critical. Commonly used in research labs, data centers, and engineering firms, this card benefits users who rely on GPU acceleration to speed up simulations, data analysis, and rendering tasks. Key Features Equipped with two GK104 Kepler GPUs to deliver strong parallel processing capabilities 8GB of high-speed GDDR5 memory to manage large datasets efficiently Supports CUDA for customizable and flexible compute solutions Designed to reduce bottlenecks in compute-heavy applications Compatible with a range of scientific and engineering software These accelerators are typically deployed in clusters or workstations where massive computational throughput is necessary. They play a crucial role in accelerating workflows that would otherwise take much longer on traditional CPUs.

Use Cases
The 90Y2338 IBM nVidia Tesla K10 accelerator is suited for various high-demand computing scenarios where GPU acceleration significantly enhances performance. How It's Used: Accelerating scientific simulations and modeling tasks Enhancing data analytics and machine learning workflows Supporting high-throughput video rendering and processing Boosting performance in financial risk modeling and analysis Driving computational workloads in engineering and research applications This accelerator is a key asset for professionals seeking to improve computational efficiency and throughput in demanding GPU-intensive tasks.